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context stringlengths 130 6.32k	question stringlengths 1.42k 1.58k	answer stringlengths 2 496k	max_new_tokens int64 2.05k 2.05k	answer_prefix stringclasses 1 value
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["70", "chevrolet", "chevrolet impala"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["67", "volkswagen", "volkswagen type 3"], ["99", "chevrolet", "chevrolet caprice classic"], ["376", "chevrolet", "chevrolet cavalier"], ["147", "ford", "ford gran torino (sw)"], ["69", "ford", "ford pinto runabout"], ["59", "peugeot", "peugeot 304"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["78", "chrysler", "chrysler newport royal"], ["140", "chevrolet", "chevrolet vega"], ["233", "chevrolet", "chevrolet concours"], ["86", "peugeot", "peugeot 504 (sw)"], ["286", "volkswagen", "volkswagen scirocco"], ["150", "volkswagen", "volkswagen dasher"], ["141", "chevrolet", "chevrolet chevelle malibu classic"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["338", "renault", "renault lecar deluxe"], ["186", "peugeot", "peugeot 504"], ["71", "pontiac", "pontiac catalina"], ["153", "datsun", "datsun 710"], ["87", "renault", "renault 12 (sw)"], ["194", "renault", "renault 12tl"], ["281", "datsun", "datsun 200-sx"], ["82", "ford", "ford gran torino (sw)"], ["39", "ford", "ford pinto"], ["200", "chevrolet", "chevrolet nova"], ["37", "chevrolet", "chevrolet vega 2300"], ["307", "peugeot", "peugeot 504"], ["27", "peugeot", "peugeot 504"], ["7", "chevrolet", "chevrolet impala"], ["367", "peugeot", "peugeot 505s turbo diesel"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["68", "chevrolet", "chevrolet vega"], ["65", "toyota", "toyota corona hardtop"], ["229", "chevrolet", "chevrolet caprice classic"], ["374", "ford", "ford granada gl"], ["285", "peugeot", "peugeot 604sl"], ["397", "chrysler", "chrysler lebaron medallion"], ["319", "chevrolet", "chevrolet chevette"], ["356", "toyota", "toyota tercel"], ["293", "chevrolet", "chevrolet caprice classic"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["54", "chevrolet", "chevrolet vega (sw)"], ["239", "chrysler", "chrysler cordoba"], ["180", "volkswagen", "volkswagen dasher"], ["169", "chevrolet", "chevrolet chevelle malibu"], ["181", "datsun", "datsun 710"], ["198", "ford", "ford gran torino"], ["209", "pontiac", "pontiac ventura sj"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["185", "audi", "audi 100ls"], ["401", "chevrolet", "chevrolet camaro"], ["144", "ford", "ford gran torino"], ["334", "volkswagen", "vw dasher (diesel)"], ["60", "fiat", "fiat 124b"], ["317", "volkswagen", "vw rabbit"], ["96", "ford", "ford gran torino"], ["33", "chevrolet", "chevy c20"], ["275", "toyota", "toyota corona"], ["371", "datsun", "datsun 810 maxima"], ["38", "toyota", "toyota corona"], ["90", "toyota", "toyota corona mark ii (sw)"], ["217", "peugeot", "peugeot 504"], ["173", "chevrolet", "chevrolet monza 2+2"], ["88", "ford", "ford pinto (sw)"], ["399", "toyota", "toyota celica gt"], ["58", "opel", "opel 1900"], ["73", "ford", "ford galaxie 500"], ["205", "volkswagen", "vw rabbit"], ["111", "chevrolet", "chevrolet impala"], ["381", "pontiac", "pontiac phoenix"], ["110", "volkswagen", "volkswagen super beetle"], ["372", "buick", "buick century"], ["13", "ford", "ford torino (sw)"], ["106", "chevrolet", "chevrolet nova custom"], ["262", "ford", "ford fairmont (auto)"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["30", "bmw", "bmw 2002"], ["46", "chevrolet", "chevrolet impala"], ["203", "chevrolet", "chevrolet chevette"], ["394", "datsun", "datsun 310 gx"], ["40", "volkswagen", "volkswagen super beetle 117"], ["95", "chevrolet", "chevrolet malibu"], ["28", "audi", "audi 100 ls"], ["384", "volkswagen", "volkswagen rabbit l"], ["131", "toyota", "toyota mark ii"], ["382", "ford", "ford fairmont futura"], ["19", "chevrolet", "chevrolet monte carlo"], ["47", "pontiac", "pontiac catalina brougham"], ["406", "chevrolet", "chevy s-10"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["120", "ford", "ford pinto"], ["276", "datsun", "datsun 510"], ["29", "saab", "saab 99e"], ["312", "fiat", "fiat strada custom"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["335", "audi", "audi 5000s (diesel)"], ["136", "chevrolet", "chevrolet nova"], ["250", "bmw", "bmw 320i"], ["379", "pontiac", "pontiac j2000 se hatchback"], ["178", "pontiac", "pontiac astro"], ["237", "pontiac", "pontiac grand prix lj"], ["393", "honda", "honda civic (auto)"], ["62", "datsun", "datsun 1200"], ["139", "toyota", "toyota corolla 1200"], ["396", "oldsmobile", "oldsmobile cutlass ciera (diesel)"], ["370", "toyota", "toyota cressida"], ["50", "dodge", "dodge monaco (sw)"], ["155", "fiat", "fiat 128"], ["152", "toyota", "toyota corona"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["8", "nissan", "Nissan Motors", "4"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["301", "volkswagen", "vw rabbit custom"], ["278", "toyota", "toyota celica gt liftback"], ["123", "chevrolet", "chevrolet monte carlo s"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["63", "volkswagen", "volkswagen model 111"], ["118", "datsun", "datsun 610"], ["332", "datsun", "datsun 210"], ["218", "toyota", "toyota mark ii"], ["188", "saab", "saab 99le"], ["21", "toyota", "toyota corona mark ii"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["22", "kia", "Kia Motors", "8"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["216", "plymouth", "plymouth volare premier v8"], ["362", "renault", "renault 18i"], ["373", "oldsmobile", "oldsmobile cutlass ls"], ["221", "chevrolet", "chevy c10"], ["211", "volkswagen", "volkswagen rabbit"], ["89", "datsun", "datsun 510 (sw)"], ["336", "mercedes-benz", "mercedes-benz 240d"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the name of the different car makers who produced a car in 1970?	[["gm"], ["chrysler"], ["amc"], ["ford"], ["citroen"], ["toyota"], ["nissan"], ["volkswagen"], ["peugeaut"], ["saab"], ["bmw"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["144", "ford", "ford gran torino"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["198", "ford", "ford gran torino"], ["82", "ford", "ford gran torino (sw)"], ["96", "ford", "ford gran torino"], ["147", "ford", "ford gran torino (sw)"], ["191", "opel", "opel 1900"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["356", "toyota", "toyota tercel"], ["233", "chevrolet", "chevrolet concours"], ["178", "pontiac", "pontiac astro"], ["88", "ford", "ford pinto (sw)"], ["58", "opel", "opel 1900"], ["19", "chevrolet", "chevrolet monte carlo"], ["13", "ford", "ford torino (sw)"], ["190", "fiat", "fiat 131"], ["319", "chevrolet", "chevrolet chevette"], ["138", "ford", "ford pinto"], ["30", "bmw", "bmw 2002"], ["382", "ford", "ford fairmont futura"], ["338", "renault", "renault lecar deluxe"], ["275", "toyota", "toyota corona"], ["131", "toyota", "toyota mark ii"], ["155", "fiat", "fiat 128"], ["362", "renault", "renault 18i"], ["182", "ford", "ford pinto"], ["176", "ford", "ford pinto"], ["203", "chevrolet", "chevrolet chevette"], ["242", "pontiac", "pontiac sunbird coupe"], ["245", "chevrolet", "chevrolet chevette"], ["286", "volkswagen", "volkswagen scirocco"], ["87", "renault", "renault 12 (sw)"], ["69", "ford", "ford pinto runabout"], ["218", "toyota", "toyota mark ii"], ["208", "ford", "ford granada ghia"], ["173", "chevrolet", "chevrolet monza 2+2"], ["239", "chrysler", "chrysler cordoba"], ["39", "ford", "ford pinto"], ["101", "plymouth", "plymouth fury gran sedan"], ["125", "fiat", "fiat 128"], ["312", "fiat", "fiat strada custom"], ["236", "ford", "ford granada"], ["60", "fiat", "fiat 124b"], ["229", "chevrolet", "chevrolet caprice classic"], ["262", "ford", "ford fairmont (auto)"], ["181", "datsun", "datsun 710"], ["153", "datsun", "datsun 710"], ["165", "chevrolet", "chevrolet bel air"], ["216", "plymouth", "plymouth volare premier v8"], ["316", "pontiac", "pontiac phoenix"], ["117", "chevrolet", "chevrolet vega"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["372", "buick", "buick century"], ["294", "ford", "ford ltd landau"], ["111", "chevrolet", "chevrolet impala"], ["120", "ford", "ford pinto"], ["381", "pontiac", "pontiac phoenix"], ["99", "chevrolet", "chevrolet caprice classic"], ["376", "chevrolet", "chevrolet cavalier"], ["156", "fiat", "fiat 124 tc"], ["161", "chevrolet", "chevrolet nova"], ["370", "toyota", "toyota cressida"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["371", "datsun", "datsun 810 maxima"], ["276", "datsun", "datsun 510"], ["180", "volkswagen", "volkswagen dasher"], ["59", "peugeot", "peugeot 304"], ["54", "chevrolet", "chevrolet vega (sw)"], ["194", "renault", "renault 12tl"], ["221", "chevrolet", "chevy c10"], ["293", "chevrolet", "chevrolet caprice classic"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["214", "ford", "ford pinto"], ["200", "chevrolet", "chevrolet nova"], ["334", "volkswagen", "vw dasher (diesel)"], ["226", "renault", "renault 5 gtl"], ["68", "chevrolet", "chevrolet vega"], ["150", "volkswagen", "volkswagen dasher"], ["272", "ford", "ford futura"], ["5", "ford", "ford torino"], ["136", "chevrolet", "chevrolet nova"], ["21", "toyota", "toyota corona mark ii"], ["183", "volkswagen", "volkswagen rabbit"], ["124", "pontiac", "pontiac grand prix"], ["374", "ford", "ford granada gl"], ["398", "ford", "ford granada l"], ["116", "toyota", "toyota carina"], ["167", "ford", "ford ltd"], ["123", "chevrolet", "chevrolet monte carlo s"], ["204", "chevrolet", "chevrolet woody"], ["47", "pontiac", "pontiac catalina brougham"], ["237", "pontiac", "pontiac grand prix lj"], ["33", "chevrolet", "chevy c20"], ["122", "fiat", "fiat 124 sport coupe"], ["244", "ford", "ford mustang ii 2+2"], ["288", "pontiac", "pontiac lemans v6"], ["234", "buick", "buick skylark"], ["152", "toyota", "toyota corona"], ["211", "volkswagen", "volkswagen rabbit"], ["274", "chevrolet", "chevrolet chevette"], ["126", "opel", "opel manta"], ["284", "saab", "saab 99gle"], ["212", "datsun", "datsun b-210"], ["217", "peugeot", "peugeot 504"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["340", " volkswagen", "volkswagen rabbit"], ["250", "bmw", "bmw 320i"], ["71", "pontiac", "pontiac catalina"], ["168", "buick", "buick century"], ["322", "ford", "ford fairmont"], ["151", "opel", "opel manta"], ["133", "plymouth", "plymouth duster"], ["140", "chevrolet", "chevrolet vega"], ["201", "ford", "ford maverick"], ["249", "datsun", "datsun 810"], ["158", "subaru", "subaru"], ["231", "dodge", "dodge monaco brougham"], ["384", "volkswagen", "volkswagen rabbit l"], ["263", "ford", "ford fairmont (man)"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["159", "fiat", "fiat x1.9"], ["363", "honda", "honda prelude"], ["46", "chevrolet", "chevrolet impala"], ["38", "toyota", "toyota corona"], ["209", "pontiac", "pontiac ventura sj"], ["44", "ford", "ford torino 500"], ["241", "volkswagen", "volkswagen rabbit custom"], ["317", "volkswagen", "vw rabbit"], ["186", "peugeot", "peugeot 504"], ["401", "chevrolet", "chevrolet camaro"], ["179", "toyota", "toyota corona"], ["118", "datsun", "datsun 610"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["110", "volkswagen", "volkswagen super beetle"], ["248", "volkswagen", "volkswagen dasher"], ["2", "buick", "buick skylark 320"], ["283", "volvo", "volvo 264gl"], ["307", "peugeot", "peugeot 504"], ["62", "datsun", "datsun 1200"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["49", "plymouth", "plymouth fury iii"], ["51", "ford", "ford country squire (sw)"], ["25", "datsun", "datsun pl510"], ["332", "datsun", "datsun 210"], ["320", "datsun", "datsun 310"], ["174", "ford", "ford mustang ii"], ["314", "chevrolet", "chevrolet citation"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Find the make and production time of the cars that were produced in the earliest year?	[["chevrolet chevelle malibu", "1970"], ["buick skylark 320", "1970"], ["plymouth satellite", "1970"], ["amc rebel sst", "1970"], ["ford torino", "1970"], ["ford galaxie 500", "1970"], ["chevrolet impala", "1970"], ["plymouth fury iii", "1970"], ["pontiac catalina", "1970"], ["amc ambassador dpl", "1970"], ["citroen ds-21 pallas", "1970"], ["chevrolet chevelle concours [sw]", "1970"], ["ford torino [sw]", "1970"], ["plymouth satellite [sw]", "1970"], ["amc rebel sst [sw]", "1970"], ["dodge challenger se", "1970"], ["plymouth cuda 340", "1970"], ["ford mustang boss 302", "1970"], ["chevrolet monte carlo", "1970"], ["buick estate wagon [sw]", "1970"], ["toyota corona mark ii", "1970"], ["plymouth duster", "1970"], ["amc hornet", "1970"], ["ford maverick", "1970"], ["datsun pl510", "1970"], ["volkswagen 1131 deluxe sedan", "1970"], ["peugeot 504", "1970"], ["audi 100 ls", "1970"], ["saab 99e", "1970"], ["bmw 2002", "1970"], ["amc gremlin", "1970"], ["ford f250", "1970"], ["chevy c20", "1970"], ["dodge d200", "1970"], ["hi 1200d", "1970"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["98", "mercury", "mercury marquis brougham"], ["109", "plymouth", "plymouth duster"], ["133", "plymouth", "plymouth duster"], ["22", "plymouth", "plymouth duster"], ["372", "buick", "buick century"], ["199", "plymouth", "plymouth valiant"], ["387", "plymouth", "plymouth horizon miser"], ["2", "buick", "buick skylark 320"], ["235", "plymouth", "plymouth volare custom"], ["232", "mercury", "mercury cougar brougham"], ["352", "plymouth", "plymouth champ"], ["220", "cadillac", "cadillac seville"], ["105", "plymouth", "plymouth valiant"], ["49", "plymouth", "plymouth fury iii"], ["347", "buick", "buick skylark"], ["168", "buick", "buick century"], ["295", "mercury", "mercury grand marquis"], ["284", "saab", "saab 99gle"], ["234", "buick", "buick skylark"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["144", "ford", "ford gran torino"], ["58", "opel", "opel 1900"], ["338", "renault", "renault lecar deluxe"], ["346", "plymouth", "plymouth reliant"], ["356", "toyota", "toyota tercel"], ["271", "buick", "buick regal sport coupe (turbo)"], ["313", "buick", "buick skylark limited"], ["275", "toyota", "toyota corona"], ["76", "buick", "buick lesabre custom"], ["160", "plymouth", "plymouth valiant custom"], ["166", "plymouth", "plymouth grand fury"], ["298", "ford", "ford country squire (sw)"], ["72", "plymouth", "plymouth fury iii"], ["145", "buick", "buick century luxus (sw)"], ["308", "oldsmobile", "oldsmobile cutlass salon brougham"], ["75", "mercury", "mercury marquis"], ["148", "amc", "amc matador (sw)"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["45", "amc", "amc matador"], ["225", "buick", "buick opel isuzu deluxe"], ["151", "opel", "opel manta"], ["376", "chevrolet", "chevrolet cavalier"], ["142", "amc", "amc matador"], ["170", "amc", "amc matador"], ["191", "opel", "opel 1900"], ["350", "plymouth", "plymouth reliant"], ["310", "plymouth", "plymouth horizon tc3"], ["51", "ford", "ford country squire (sw)"], ["198", "ford", "ford gran torino"], ["96", "ford", "ford gran torino"], ["8", "plymouth", "plymouth fury iii"], ["233", "chevrolet", "chevrolet concours"], ["176", "ford", "ford pinto"], ["289", "mercury", "mercury zephyr 6"], ["20", "buick", "buick estate wagon (sw)"], ["197", "amc", "amc matador"], ["266", "buick", "buick century special"], ["93", "buick", "buick century 350"], ["88", "ford", "ford pinto (sw)"], ["121", "mercury", "mercury capri v6"], ["196", "dodge", "dodge coronet brougham"], ["82", "ford", "ford gran torino (sw)"], ["184", "amc", "amc pacer"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["231", "dodge", "dodge monaco brougham"], ["138", "ford", "ford pinto"], ["182", "ford", "ford pinto"], ["259", "mercury", "mercury monarch ghia"], ["103", "buick", "buick electra 225 custom"], ["69", "ford", "ford pinto runabout"], ["11", "citroen", "citroen ds-21 pallas"], ["126", "opel", "opel manta"], ["263", "ford", "ford fairmont (man)"], ["264", "plymouth", "plymouth volare"], ["395", "buick", "buick century limited"], ["155", "fiat", "fiat 128"], ["319", "chevrolet", "chevrolet chevette"], ["80", "amc", "amc matador (sw)"], ["188", "saab", "saab 99le"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["279", "plymouth", "plymouth sapporo"], ["172", "buick", "buick skyhawk"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["21", "toyota", "toyota corona mark ii"], ["171", "plymouth", "plymouth fury"], ["147", "ford", "ford gran torino (sw)"], ["283", "volvo", "volvo 264gl"], ["335", "audi", "audi 5000s (diesel)"], ["227", "plymouth", "plymouth arrow gs"], ["47", "pontiac", "pontiac catalina brougham"], ["57", "mercury", "mercury capri 2000"], ["66", "dodge", "dodge colt hardtop"], ["94", "amc", "amc matador"], ["383", "amc", "amc concord dl"], ["101", "plymouth", "plymouth fury gran sedan"], ["216", "plymouth", "plymouth volare premier v8"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["375", "chrysler", "chrysler lebaron salon"], ["324", "dodge", "dodge aspen"], ["193", "dodge", "dodge colt"], ["39", "ford", "ford pinto"], ["208", "ford", "ford granada ghia"], ["130", "saab", "saab 99le"], ["120", "ford", "ford pinto"], ["258", "dodge", "dodge diplomat"], ["257", "oldsmobile", "oldsmobile cutlass salon brougham"], ["239", "chrysler", "chrysler cordoba"], ["236", "ford", "ford granada"], ["221", "chevrolet", "chevy c10"], ["268", "dodge", "dodge aspen"], ["17", "plymouth", "plymouth cuda 340"], ["65", "toyota", "toyota corona hardtop"], ["78", "chrysler", "chrysler newport royal"], ["294", "ford", "ford ltd landau"], ["179", "toyota", "toyota corona"], ["267", "mercury", "mercury zephyr"], ["104", "amc", "amc ambassador brougham"], ["38", "toyota", "toyota corona"], ["374", "ford", "ford granada gl"], ["87", "renault", "renault 12 (sw)"], ["291", "amc", "amc concord dl 6"], ["241", "volkswagen", "volkswagen rabbit custom"], ["380", "dodge", "dodge aries se"], ["162", "mercury", "mercury monarch"], ["331", "dodge", "dodge colt"], ["178", "pontiac", "pontiac astro"], ["382", "ford", "ford fairmont futura"], ["312", "fiat", "fiat strada custom"], ["190", "fiat", "fiat 131"], ["192", "capri", "capri ii"], ["388", "mercury", "mercury lynx l"], ["306", "cadillac", "cadillac eldorado"], ["204", "chevrolet", "chevrolet woody"], ["210", "amc", "amc pacer d/l"], ["177", "amc", "amc gremlin"], ["30", "bmw", "bmw 2002"], ["90", "toyota", "toyota corona mark ii (sw)"], ["200", "chevrolet", "chevrolet nova"], ["269", "amc", "amc concord d/l"], ["29", "saab", "saab 99e"], ["153", "datsun", "datsun 710"], ["276", "datsun", "datsun 510"], ["229", "chevrolet", "chevrolet caprice classic"], ["91", "dodge", "dodge colt (sw)"], ["297", "buick", "buick estate wagon (sw)"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the maker of the carr produced in the earliest year and what year was it?	[["chevrolet chevelle malibu", "1970"], ["buick skylark 320", "1970"], ["plymouth satellite", "1970"], ["amc rebel sst", "1970"], ["ford torino", "1970"], ["ford galaxie 500", "1970"], ["chevrolet impala", "1970"], ["plymouth fury iii", "1970"], ["pontiac catalina", "1970"], ["amc ambassador dpl", "1970"], ["citroen ds-21 pallas", "1970"], ["chevrolet chevelle concours [sw]", "1970"], ["ford torino [sw]", "1970"], ["plymouth satellite [sw]", "1970"], ["amc rebel sst [sw]", "1970"], ["dodge challenger se", "1970"], ["plymouth cuda 340", "1970"], ["ford mustang boss 302", "1970"], ["chevrolet monte carlo", "1970"], ["buick estate wagon [sw]", "1970"], ["toyota corona mark ii", "1970"], ["plymouth duster", "1970"], ["amc hornet", "1970"], ["ford maverick", "1970"], ["datsun pl510", "1970"], ["volkswagen 1131 deluxe sedan", "1970"], ["peugeot 504", "1970"], ["audi 100 ls", "1970"], ["saab 99e", "1970"], ["bmw 2002", "1970"], ["amc gremlin", "1970"], ["ford f250", "1970"], ["chevy c20", "1970"], ["dodge d200", "1970"], ["hi 1200d", "1970"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["82", "ford", "ford gran torino (sw)"], ["161", "chevrolet", "chevrolet nova"], ["165", "chevrolet", "chevrolet bel air"], ["169", "chevrolet", "chevrolet chevelle malibu"], ["209", "pontiac", "pontiac ventura sj"], ["173", "chevrolet", "chevrolet monza 2+2"], ["356", "toyota", "toyota tercel"], ["216", "plymouth", "plymouth volare premier v8"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["217", "peugeot", "peugeot 504"], ["198", "ford", "ford gran torino"], ["68", "chevrolet", "chevrolet vega"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["87", "renault", "renault 12 (sw)"], ["70", "chevrolet", "chevrolet impala"], ["21", "toyota", "toyota corona mark ii"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["147", "ford", "ford gran torino (sw)"], ["110", "volkswagen", "volkswagen super beetle"], ["54", "chevrolet", "chevrolet vega (sw)"], ["174", "ford", "ford mustang ii"], ["334", "volkswagen", "vw dasher (diesel)"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["95", "chevrolet", "chevrolet malibu"], ["218", "toyota", "toyota mark ii"], ["96", "ford", "ford gran torino"], ["204", "chevrolet", "chevrolet woody"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["180", "volkswagen", "volkswagen dasher"], ["86", "peugeot", "peugeot 504 (sw)"], ["285", "peugeot", "peugeot 604sl"], ["144", "ford", "ford gran torino"], ["43", "chevrolet", "chevrolet chevelle malibu"], ["239", "chrysler", "chrysler cordoba"], ["90", "toyota", "toyota corona mark ii (sw)"], ["229", "chevrolet", "chevrolet caprice classic"], ["293", "chevrolet", "chevrolet caprice classic"], ["84", "volvo", "volvo 145e (sw)"], ["18", "ford", "ford mustang boss 302"], ["40", "volkswagen", "volkswagen super beetle 117"], ["194", "renault", "renault 12tl"], ["30", "bmw", "bmw 2002"], ["59", "peugeot", "peugeot 304"], ["208", "ford", "ford granada ghia"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["340", " volkswagen", "volkswagen rabbit"], ["221", "chevrolet", "chevy c10"], ["203", "chevrolet", "chevrolet chevette"], ["286", "volkswagen", "volkswagen scirocco"], ["240", "ford", "ford thunderbird"], ["211", "volkswagen", "volkswagen rabbit"], ["140", "chevrolet", "chevrolet vega"], ["362", "renault", "renault 18i"], ["344", "ford", "ford mustang cobra"], ["80", "amc", "amc matador (sw)"], ["7", "chevrolet", "chevrolet impala"], ["13", "ford", "ford torino (sw)"], ["376", "chevrolet", "chevrolet cavalier"], ["19", "chevrolet", "chevrolet monte carlo"], ["46", "chevrolet", "chevrolet impala"], ["106", "chevrolet", "chevrolet nova custom"], ["371", "datsun", "datsun 810 maxima"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["26", "16", "renault"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["179", "toyota", "toyota corona"], ["200", "chevrolet", "chevrolet nova"], ["131", "toyota", "toyota mark ii"], ["89", "datsun", "datsun 510 (sw)"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["370", "toyota", "toyota cressida"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["205", "volkswagen", "vw rabbit"], ["318", "toyota", "toyota corolla tercel"], ["338", "renault", "renault lecar deluxe"], ["226", "renault", "renault 5 gtl"], ["50", "dodge", "dodge monaco (sw)"], ["78", "chrysler", "chrysler newport royal"], ["141", "chevrolet", "chevrolet chevelle malibu classic"], ["275", "toyota", "toyota corona"], ["152", "toyota", "toyota corona"], ["274", "chevrolet", "chevrolet chevette"], ["178", "pontiac", "pontiac astro"], ["327", "mazda", "mazda 626"], ["278", "toyota", "toyota celica gt liftback"], ["328", "datsun", "datsun 510 hatchback"], ["299", "chevrolet", "chevrolet malibu classic (sw)"], ["117", "chevrolet", "chevrolet vega"], ["305", "mercedes", "mercedes benz 300d"], ["183", "volkswagen", "volkswagen rabbit"], ["69", "ford", "ford pinto runabout"], ["261", "chevrolet", "chevrolet malibu"], ["231", "dodge", "dodge monaco brougham"], ["245", "chevrolet", "chevrolet chevette"], ["88", "ford", "ford pinto (sw)"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["116", "toyota", "toyota carina"], ["312", "fiat", "fiat strada custom"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["16", "dodge", "dodge challenger se"], ["99", "chevrolet", "chevrolet caprice classic"], ["319", "chevrolet", "chevrolet chevette"], ["37", "chevrolet", "chevrolet vega 2300"], ["213", "toyota", "toyota corolla"], ["288", "pontiac", "pontiac lemans v6"], ["67", "volkswagen", "volkswagen type 3"], ["38", "toyota", "toyota corona"], ["122", "fiat", "fiat 124 sport coupe"], ["307", "peugeot", "peugeot 504"], ["186", "peugeot", "peugeot 504"], ["63", "volkswagen", "volkswagen model 111"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["16", "12", "mazda"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["150", "volkswagen", "volkswagen dasher"], ["244", "ford", "ford mustang ii 2+2"], ["164", "pontiac", "pontiac catalina"], ["248", "volkswagen", "volkswagen dasher"], ["228", "datsun", "datsun f-10 hatchback"], ["314", "chevrolet", "chevrolet citation"], ["185", "audi", "audi 100ls"], ["317", "volkswagen", "vw rabbit"], ["406", "chevrolet", "chevy s-10"], ["401", "chevrolet", "chevrolet camaro"], ["52", "pontiac", "pontiac safari (sw)"], ["273", "dodge", "dodge magnum xe"], ["101", "plymouth", "plymouth fury gran sedan"], ["175", "toyota", "toyota corolla"], ["233", "chevrolet", "chevrolet concours"], ["27", "peugeot", "peugeot 504"], ["48", "ford", "ford galaxie 500"], ["123", "chevrolet", "chevrolet monte carlo s"], ["335", "audi", "audi 5000s (diesel)"], ["73", "ford", "ford galaxie 500"], ["326", "toyota", "toyota corona liftback"], ["237", "pontiac", "pontiac grand prix lj"], ["212", "datsun", "datsun b-210"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["241", "volkswagen", "volkswagen rabbit custom"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Which distinct car models are the produced after 1980?	[["plymouth"], ["buick"], ["dodge"], ["chevrolet"], ["toyota"], ["honda"], ["subaru"], ["datsun"], ["mazda"], ["ford"], ["volkswagen"], ["renault"], ["peugeot"], ["saab"], ["volvo"], ["oldsmobile"], ["chrysler"], ["pontiac"], ["amc"], ["mercury"], ["nissan"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["80", "amc", "amc matador (sw)"], ["160", "plymouth", "plymouth valiant custom"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["87", "renault", "renault 12 (sw)"], ["72", "plymouth", "plymouth fury iii"], ["380", "dodge", "dodge aries se"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["318", "38.1", "4", "89.0", "60", "1968", "18.8", "1980"], ["285", "16.2", "6", "163.0", "133", "3410", "15.8", "1978"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["89", "datsun", "datsun 510 (sw)"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["332", "40.8", "4", "85.0", "65", "2110", "19.2", "1980"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["148", "amc", "amc matador (sw)"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["327", "31.3", "4", "120.0", "75", "2542", "17.5", "1980"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["212", "datsun", "datsun b-210"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["371", "datsun", "datsun 810 maxima"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["382", "24", "4", "140.0", "92", "2865", "16.4", "1982"], ["379", "31", "4", "112.0", "85", "2575", "16.2", "1982"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["199", "plymouth", "plymouth valiant"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["209", "18.5", "6", "250.0", "110", "3645", "16.2", "1976"], ["322", "26.4", "4", "140.0", "88", "2870", "18.1", "1980"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"], ["329", "32.2", "4", "108.0", "75", "2265", "15.2", "1980"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["166", "plymouth", "plymouth grand fury"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["385", "37", "4", "91.0", "68", "2025", "18.2", "1982"], ["219", "16.5", "6", "168.0", "120", "3820", "16.7", "1976"], ["319", "32.1", "4", "98.0", "70", "2120", "15.5", "1980"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["209", "pontiac", "pontiac ventura sj"], ["68", "chevrolet", "chevrolet vega"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["205", "29", "4", "90.0", "70", "1937", "14.2", "1976"], ["290", "22.3", "4", "140.0", "88", "2890", "17.3", "1979"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["280", "oldsmobile", "oldsmobile starfire sx"], ["171", "plymouth", "plymouth fury"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["317", "41.5", "4", "98.0", "76", "2144", "14.7", "1980"], ["296", "18.2", "8", "318.0", "135", "3830", "15.2", "1979"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["338", "renault", "renault lecar deluxe"], ["105", "plymouth", "plymouth valiant"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["390", "36", "4", "107.0", "75", "2205", "14.5", "1982"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["285", "peugeot", "peugeot 604sl"], ["210", "amc", "amc pacer d/l"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["392", "38", "4", "91.0", "67", "1965", "15.0", "1982"], ["217", "19", "4", "120.0", "88", "3270", "21.9", "1976"], ["293", "17", "8", "305.0", "130", "3840", "15.4", "1979"], ["297", "16.9", "8", "350.0", "155", "4360", "14.9", "1979"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["88", "22", "4", "122.0", "86", "2395", "16.0", "1972"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["174", "ford", "ford mustang ii"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["387", "38", "4", "105.0", "63", "2125", "14.7", "1982"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["94", "amc", "amc matador"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["220", "16.5", "8", "350.0", "180", "4380", "12.1", "1976"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["249", "datsun", "datsun 810"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["312", "37.3", "4", "91.0", "69", "2130", "14.7", "1979"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the different models for the cards produced after 1980?	[["plymouth"], ["buick"], ["dodge"], ["chevrolet"], ["toyota"], ["honda"], ["subaru"], ["datsun"], ["mazda"], ["ford"], ["volkswagen"], ["renault"], ["peugeot"], ["saab"], ["volvo"], ["oldsmobile"], ["chrysler"], ["pontiac"], ["amc"], ["mercury"], ["nissan"]]	2,048	Answer:
Table car_makers: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"], ["13", "daimler benz", "Daimler Benz", "2"], ["8", "nissan", "Nissan Motors", "4"], ["6", "chrysler", "Chrysler", "1"], ["16", "renault", "Renault", "3"], ["4", "gm", "General Motors", "1"], ["21", "volvo", "Volvo", "6"]]Table CONTINENTS: [["ContId", "Continent"], ["2", "europe"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"]]Table CONTINENTS: [["ContId", "Continent"], ["3", "asia"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["22", "kia", "Kia Motors", "8"], ["11", "honda", "Honda", "4"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["11", "australia", "5"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["3", "bmw", "BMW", "2"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["2", "germany", "2"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"], ["9", "fiat", "Fiat", "5"], ["5", "ford", "Ford Motor Company", "1"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["6", "sweden", "2"], ["9", "russia", "2"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["23", "hyundai", "Hyundai", "8"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["3", "france", "2"]]Table CONTINENTS: [["ContId", "Continent"], ["4", "africa"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["13", "egypt", "4"], ["12", "new zealand", "5"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["19", "toyota", "Toyota", "4"], ["18", "subaru", "Subaru", "4"]]Table CONTINENTS: [["ContId", "Continent"], ["5", "australia"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["2", "volkswagen", "Volkswagen", "2"]]Table CONTINENTS: [["ContId", "Continent"], ["1", "america"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["5", "italy", "2"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"], ["7", "citroen", "Citroen", "3"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["7", "uk", "2"], ["8", "korea", "3"], ["1", "usa", "1"], ["4", "japan", "3"], ["14", "mexico", "1"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["10", "nigeria", "4"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:How many car makers are there in each continents? List the continent name and the count.	[["america", "4"], ["asia", "7"], ["europe", "11"]]	2,048	Answer:
Table car_makers: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"], ["8", "nissan", "Nissan Motors", "4"], ["1", "amc", "American Motor Company", "1"], ["16", "renault", "Renault", "3"], ["22", "kia", "Kia Motors", "8"], ["4", "gm", "General Motors", "1"], ["6", "chrysler", "Chrysler", "1"], ["3", "bmw", "BMW", "2"]]Table CONTINENTS: [["ContId", "Continent"], ["4", "africa"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["21", "volvo", "Volvo", "6"]]Table CONTINENTS: [["ContId", "Continent"], ["3", "asia"], ["2", "europe"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"], ["18", "subaru", "Subaru", "4"], ["19", "toyota", "Toyota", "4"], ["23", "hyundai", "Hyundai", "8"], ["9", "fiat", "Fiat", "5"], ["12", "mazda", "Mazda", "4"], ["2", "volkswagen", "Volkswagen", "2"], ["11", "honda", "Honda", "4"], ["5", "ford", "Ford Motor Company", "1"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["2", "germany", "2"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["7", "citroen", "Citroen", "3"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["3", "france", "2"], ["15", "brazil", "1"], ["13", "egypt", "4"], ["11", "australia", "5"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"], ["12", "new zealand", "5"], ["6", "sweden", "2"]]Table CONTINENTS: [["ContId", "Continent"], ["5", "australia"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["5", "italy", "2"], ["4", "japan", "3"], ["8", "korea", "3"], ["14", "mexico", "1"], ["10", "nigeria", "4"]]Table CONTINENTS: [["ContId", "Continent"], ["1", "america"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["7", "uk", "2"], ["1", "usa", "1"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the name of each continent and how many car makers are there in each one?	[["america", "4"], ["asia", "7"], ["europe", "11"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["8", "nissan", "Nissan Motors", "4"], ["13", "daimler benz", "Daimler Benz", "2"], ["22", "kia", "Kia Motors", "8"], ["4", "gm", "General Motors", "1"], ["16", "renault", "Renault", "3"], ["1", "amc", "American Motor Company", "1"], ["7", "citroen", "Citroen", "3"], ["2", "volkswagen", "Volkswagen", "2"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["2", "germany", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["21", "volvo", "Volvo", "6"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["19", "toyota", "Toyota", "4"], ["5", "ford", "Ford Motor Company", "1"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["23", "hyundai", "Hyundai", "8"], ["3", "bmw", "BMW", "2"], ["14", "opel", "Opel", "2"], ["9", "fiat", "Fiat", "5"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["3", "france", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["11", "honda", "Honda", "4"], ["6", "chrysler", "Chrysler", "1"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["5", "italy", "2"], ["8", "korea", "3"], ["6", "sweden", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["12", "new zealand", "5"], ["4", "japan", "3"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"], ["18", "subaru", "Subaru", "4"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["11", "australia", "5"], ["10", "nigeria", "4"], ["7", "uk", "2"], ["14", "mexico", "1"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["13", "egypt", "4"], ["1", "usa", "1"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Which of the countries has the most car makers? List the country name.	[["japan"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["8", "nissan", "Nissan Motors", "4"], ["13", "daimler benz", "Daimler Benz", "2"], ["16", "renault", "Renault", "3"], ["22", "kia", "Kia Motors", "8"], ["4", "gm", "General Motors", "1"], ["21", "volvo", "Volvo", "6"], ["19", "toyota", "Toyota", "4"], ["2", "volkswagen", "Volkswagen", "2"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["7", "citroen", "Citroen", "3"], ["23", "hyundai", "Hyundai", "8"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"], ["2", "germany", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"], ["5", "ford", "Ford Motor Company", "1"], ["3", "bmw", "BMW", "2"], ["9", "fiat", "Fiat", "5"], ["6", "chrysler", "Chrysler", "1"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["3", "france", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"], ["11", "honda", "Honda", "4"], ["18", "subaru", "Subaru", "4"], ["14", "opel", "Opel", "2"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["5", "italy", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["6", "sweden", "2"], ["12", "new zealand", "5"], ["8", "korea", "3"], ["4", "japan", "3"], ["14", "mexico", "1"], ["10", "nigeria", "4"], ["11", "australia", "5"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["13", "egypt", "4"], ["7", "uk", "2"], ["1", "usa", "1"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the name of the country with the most car makers?	[["japan"]]	2,048	Answer:
Table car_makers: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"], ["1", "amc", "American Motor Company", "1"], ["8", "nissan", "Nissan Motors", "4"], ["22", "kia", "Kia Motors", "8"]]Table model_list: [["ModelId", "Maker", "Model"], ["26", "16", "renault"], ["31", "2", "volkswagen"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["16", "renault", "Renault", "3"], ["2", "volkswagen", "Volkswagen", "2"], ["4", "gm", "General Motors", "1"]]Table model_list: [["ModelId", "Maker", "Model"], ["23", "15", "peugeot"], ["8", "6", "chrysler"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["5", "ford", "Ford Motor Company", "1"]]Table model_list: [["ModelId", "Maker", "Model"], ["7", "4", "chevrolet"], ["20", "8", "nissan"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["6", "chrysler", "Chrysler", "1"]]Table model_list: [["ModelId", "Maker", "Model"], ["3", "3", "bmw"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"]]Table model_list: [["ModelId", "Maker", "Model"], ["22", "14", "opel"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["3", "bmw", "BMW", "2"], ["21", "volvo", "Volvo", "6"]]Table model_list: [["ModelId", "Maker", "Model"], ["32", "21", "volvo"], ["29", "19", "toyota"], ["2", "2", "audi"], ["18", "13", "mercedes-benz"], ["15", "11", "honda"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["23", "hyundai", "Hyundai", "8"]]Table model_list: [["ModelId", "Maker", "Model"], ["25", "4", "pontiac"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["19", "toyota", "Toyota", "4"]]Table model_list: [["ModelId", "Maker", "Model"], ["17", "13", "mercedes"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["11", "honda", "Honda", "4"], ["7", "citroen", "Citroen", "3"]]Table model_list: [["ModelId", "Maker", "Model"], ["13", "5", "ford"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["18", "subaru", "Subaru", "4"], ["9", "fiat", "Fiat", "5"]]Table model_list: [["ModelId", "Maker", "Model"], ["12", "9", "fiat"], ["9", "7", "citroen"], ["33", "22", "kia"], ["34", "23", "hyundai"], ["10", "8", "datsun"], ["28", "18", "subaru"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"]]Table model_list: [["ModelId", "Maker", "Model"], ["1", "1", "amc"], ["4", "4", "buick"], ["24", "6", "plymouth"], ["16", "12", "mazda"], ["5", "4", "cadillac"], ["21", "4", "oldsmobile"], ["27", "17", "saab"], ["35", "6", "jeep"], ["11", "6", "dodge"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"], ["20", "triumph", "Triumph", "7"], ["15", "peugeaut", "Peugeaut", "3"]]Table model_list: [["ModelId", "Maker", "Model"], ["6", "5", "capri"], ["30", "20", "triumph"], ["36", "19", "scion"], ["19", "5", "mercury"], ["14", "10", "hi"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:How many car models are produced by each maker ? Only list the count and the maker full name .	[["1", "American Motor Company"], ["2", "Volkswagen"], ["1", "BMW"], ["5", "General Motors"], ["3", "Ford Motor Company"], ["4", "Chrysler"], ["1", "Citroen"], ["2", "Nissan Motors"], ["1", "Fiat"], ["1", "Honda"], ["1", "Mazda"], ["2", "Daimler Benz"], ["1", "Opel"], ["1", "Peugeaut"], ["1", "Renault"], ["1", "Saab"], ["1", "Subaru"], ["2", "Toyota"], ["1", "Triumph"], ["1", "Volvo"], ["1", "Kia Motors"], ["1", "Hyundai"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"], ["8", "nissan", "Nissan Motors", "4"], ["22", "kia", "Kia Motors", "8"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"], ["26", "16", "renault"], ["23", "15", "peugeot"], ["7", "4", "chevrolet"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["16", "renault", "Renault", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["18", "13", "mercedes-benz"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["2", "volkswagen", "Volkswagen", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["20", "8", "nissan"], ["29", "19", "toyota"], ["8", "6", "chrysler"], ["32", "21", "volvo"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["23", "hyundai", "Hyundai", "8"], ["21", "volvo", "Volvo", "6"], ["18", "subaru", "Subaru", "4"], ["19", "toyota", "Toyota", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["28", "18", "subaru"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["22", "14", "opel"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"], ["6", "chrysler", "Chrysler", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["25", "4", "pontiac"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["34", "23", "hyundai"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["5", "ford", "Ford Motor Company", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["17", "13", "mercedes"], ["3", "3", "bmw"], ["15", "11", "honda"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["3", "bmw", "BMW", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["16", "12", "mazda"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"], ["7", "citroen", "Citroen", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["2", "2", "audi"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["11", "honda", "Honda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["12", "9", "fiat"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["21", "4", "oldsmobile"], ["35", "6", "jeep"], ["13", "5", "ford"], ["10", "8", "datsun"], ["33", "22", "kia"], ["9", "7", "citroen"], ["4", "4", "buick"], ["5", "4", "cadillac"], ["27", "17", "saab"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["24", "6", "plymouth"], ["11", "6", "dodge"], ["1", "1", "amc"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["36", "19", "scion"], ["6", "5", "capri"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["30", "20", "triumph"], ["19", "5", "mercury"], ["14", "10", "hi"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the number of car models that are produced by each maker and what is the id and full name of each maker?	[["1", "American Motor Company", "1"], ["2", "Volkswagen", "2"], ["1", "BMW", "3"], ["5", "General Motors", "4"], ["3", "Ford Motor Company", "5"], ["4", "Chrysler", "6"], ["1", "Citroen", "7"], ["2", "Nissan Motors", "8"], ["1", "Fiat", "9"], ["1", "Honda", "11"], ["1", "Mazda", "12"], ["2", "Daimler Benz", "13"], ["1", "Opel", "14"], ["1", "Peugeaut", "15"], ["1", "Renault", "16"], ["1", "Saab", "17"], ["1", "Subaru", "18"], ["2", "Toyota", "19"], ["1", "Triumph", "20"], ["1", "Volvo", "21"], ["1", "Kia Motors", "22"], ["1", "Hyundai", "23"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["53", "amc", "amc hornet sportabout (sw)"], ["135", "amc", "amc hornet"], ["202", "amc", "amc hornet"], ["107", "amc", "amc hornet"], ["80", "amc", "amc matador (sw)"], ["23", "amc", "amc hornet"], ["148", "amc", "amc matador (sw)"], ["184", "amc", "amc pacer"], ["15", "amc", "amc rebel sst (sw)"], ["170", "amc", "amc matador"], ["91", "dodge", "dodge colt (sw)"], ["210", "amc", "amc pacer d/l"], ["94", "amc", "amc matador"], ["177", "amc", "amc gremlin"], ["380", "dodge", "dodge aries se"], ["45", "amc", "amc matador"], ["142", "amc", "amc matador"], ["197", "amc", "amc matador"], ["82", "ford", "ford gran torino (sw)"], ["269", "amc", "amc concord d/l"], ["348", "dodge", "dodge aries wagon (sw)"], ["16", "dodge", "dodge challenger se"], ["129", "dodge", "dodge dart custom"], ["273", "dodge", "dodge magnum xe"], ["50", "dodge", "dodge monaco (sw)"], ["146", "dodge", "dodge coronet custom (sw)"], ["304", "amc", "amc spirit dl"], ["115", "amc", "amc gremlin"], ["207", "dodge", "dodge aspen se"], ["4", "amc", "amc rebel sst"], ["265", "amc", "amc concord"], ["193", "dodge", "dodge colt"], ["147", "ford", "ford gran torino (sw)"], ["291", "amc", "amc concord dl 6"], ["69", "ford", "ford pinto runabout"], ["41", "amc", "amc gremlin"], ["145", "buick", "buick century luxus (sw)"], ["268", "dodge", "dodge aspen"], ["383", "amc", "amc concord dl"], ["74", "amc", "amc ambassador sst"], ["400", "dodge", "dodge charger 2.2"], ["201", "ford", "ford maverick"], ["90", "toyota", "toyota corona mark ii (sw)"], ["31", "amc", "amc gremlin"], ["284", "saab", "saab 99gle"], ["110", "volkswagen", "volkswagen super beetle"], ["88", "ford", "ford pinto (sw)"], ["344", "ford", "ford mustang cobra"], ["223", "dodge", "dodge d100"], ["246", "dodge", "dodge colt m/m"], ["84", "volvo", "volvo 145e (sw)"], ["13", "ford", "ford torino (sw)"], ["244", "ford", "ford mustang ii 2+2"], ["331", "dodge", "dodge colt"], ["188", "saab", "saab 99le"], ["172", "buick", "buick skyhawk"], ["275", "toyota", "toyota corona"], ["292", "dodge", "dodge aspen 6"], ["29", "saab", "saab 99e"], ["277", "dodge", "dodge omni"], ["150", "volkswagen", "volkswagen dasher"], ["163", "ford", "ford maverick"], ["323", "amc", "amc concord"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["104", "amc", "amc ambassador brougham"], ["180", "volkswagen", "volkswagen dasher"], ["271", "buick", "buick regal sport coupe (turbo)"], ["225", "buick", "buick opel isuzu deluxe"], ["18", "ford", "ford mustang boss 302"], ["154", "dodge", "dodge colt"], ["130", "saab", "saab 99le"], ["299", "chevrolet", "chevrolet malibu classic (sw)"], ["144", "ford", "ford gran torino"], ["174", "ford", "ford mustang ii"], ["360", "ford", "ford escort 2h"], ["108", "ford", "ford maverick"], ["334", "volkswagen", "vw dasher (diesel)"], ["258", "dodge", "dodge diplomat"], ["368", "saab", "saab 900s"], ["231", "dodge", "dodge monaco brougham"], ["34", "dodge", "dodge d200"], ["66", "dodge", "dodge colt hardtop"], ["198", "ford", "ford gran torino"], ["196", "dodge", "dodge coronet brougham"], ["24", "ford", "ford maverick"], ["134", "ford", "ford maverick"], ["303", "dodge", "dodge colt hatchback custom"], ["324", "dodge", "dodge aspen"], ["97", "dodge", "dodge coronet custom"], ["300", "chrysler", "chrysler lebaron town @ country (sw)"], ["240", "ford", "ford thunderbird"], ["40", "volkswagen", "volkswagen super beetle 117"], ["297", "buick", "buick estate wagon (sw)"], ["21", "toyota", "toyota corona mark ii"], ["160", "plymouth", "plymouth valiant custom"], ["52", "pontiac", "pontiac safari (sw)"], ["149", "audi", "audi fox"], ["216", "plymouth", "plymouth volare premier v8"], ["101", "plymouth", "plymouth fury gran sedan"], ["56", "ford", "ford mustang"], ["10", "amc", "amc ambassador dpl"], ["178", "pontiac", "pontiac astro"], ["286", "volkswagen", "volkswagen scirocco"], ["165", "chevrolet", "chevrolet bel air"], ["355", "datsun", "datsun 210 mpg"], ["248", "volkswagen", "volkswagen dasher"], ["96", "ford", "ford gran torino"], ["20", "buick", "buick estate wagon (sw)"], ["204", "chevrolet", "chevrolet woody"], ["189", "honda", "honda civic cvcc"], ["260", "pontiac", "pontiac phoenix lj"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["294", "ford", "ford ltd landau"], ["54", "chevrolet", "chevrolet vega (sw)"], ["72", "plymouth", "plymouth fury iii"], ["103", "buick", "buick electra 225 custom"], ["313", "buick", "buick skylark limited"], ["209", "pontiac", "pontiac ventura sj"], ["382", "ford", "ford fairmont futura"], ["262", "ford", "ford fairmont (auto)"], ["404", "dodge", "dodge rampage"], ["402", "ford", "ford mustang gl"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["381", "pontiac", "pontiac phoenix"], ["182", "ford", "ford pinto"], ["272", "ford", "ford futura"], ["285", "peugeot", "peugeot 604sl"], ["185", "audi", "audi 100ls"], ["86", "peugeot", "peugeot 504 (sw)"], ["375", "chrysler", "chrysler lebaron salon"], ["49", "plymouth", "plymouth fury iii"], ["215", "volvo", "volvo 245"], ["290", "ford", "ford fairmont 4"], ["296", "dodge", "dodge st. regis"], ["398", "ford", "ford granada l"], ["179", "toyota", "toyota corona"], ["326", "toyota", "toyota corona liftback"], ["372", "buick", "buick century"], ["59", "peugeot", "peugeot 304"], ["239", "chrysler", "chrysler cordoba"], ["65", "toyota", "toyota corona hardtop"], ["397", "chrysler", "chrysler lebaron medallion"], ["173", "chevrolet", "chevrolet monza 2+2"], ["222", "ford", "ford f108"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the accelerate of the car make amc hornet sportabout (sw)?	[["13.5"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["53", "amc", "amc hornet sportabout (sw)"], ["135", "amc", "amc hornet"], ["107", "amc", "amc hornet"], ["202", "amc", "amc hornet"], ["80", "amc", "amc matador (sw)"], ["23", "amc", "amc hornet"], ["148", "amc", "amc matador (sw)"], ["184", "amc", "amc pacer"], ["15", "amc", "amc rebel sst (sw)"], ["170", "amc", "amc matador"], ["210", "amc", "amc pacer d/l"], ["94", "amc", "amc matador"], ["91", "dodge", "dodge colt (sw)"], ["177", "amc", "amc gremlin"], ["142", "amc", "amc matador"], ["197", "amc", "amc matador"], ["45", "amc", "amc matador"], ["380", "dodge", "dodge aries se"], ["269", "amc", "amc concord d/l"], ["348", "dodge", "dodge aries wagon (sw)"], ["82", "ford", "ford gran torino (sw)"], ["50", "dodge", "dodge monaco (sw)"], ["16", "dodge", "dodge challenger se"], ["129", "dodge", "dodge dart custom"], ["146", "dodge", "dodge coronet custom (sw)"], ["273", "dodge", "dodge magnum xe"], ["304", "amc", "amc spirit dl"], ["115", "amc", "amc gremlin"], ["4", "amc", "amc rebel sst"], ["207", "dodge", "dodge aspen se"], ["265", "amc", "amc concord"], ["400", "dodge", "dodge charger 2.2"], ["291", "amc", "amc concord dl 6"], ["110", "volkswagen", "volkswagen super beetle"], ["41", "amc", "amc gremlin"], ["145", "buick", "buick century luxus (sw)"], ["193", "dodge", "dodge colt"], ["69", "ford", "ford pinto runabout"], ["90", "toyota", "toyota corona mark ii (sw)"], ["147", "ford", "ford gran torino (sw)"], ["268", "dodge", "dodge aspen"], ["172", "buick", "buick skyhawk"], ["383", "amc", "amc concord dl"], ["201", "ford", "ford maverick"], ["150", "volkswagen", "volkswagen dasher"], ["223", "dodge", "dodge d100"], ["74", "amc", "amc ambassador sst"], ["275", "toyota", "toyota corona"], ["344", "ford", "ford mustang cobra"], ["31", "amc", "amc gremlin"], ["180", "volkswagen", "volkswagen dasher"], ["244", "ford", "ford mustang ii 2+2"], ["284", "saab", "saab 99gle"], ["88", "ford", "ford pinto (sw)"], ["246", "dodge", "dodge colt m/m"], ["13", "ford", "ford torino (sw)"], ["104", "amc", "amc ambassador brougham"], ["323", "amc", "amc concord"], ["163", "ford", "ford maverick"], ["188", "saab", "saab 99le"], ["271", "buick", "buick regal sport coupe (turbo)"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["40", "volkswagen", "volkswagen super beetle 117"], ["292", "dodge", "dodge aspen 6"], ["84", "volvo", "volvo 145e (sw)"], ["334", "volkswagen", "vw dasher (diesel)"], ["29", "saab", "saab 99e"], ["300", "chrysler", "chrysler lebaron town @ country (sw)"], ["299", "chevrolet", "chevrolet malibu classic (sw)"], ["277", "dodge", "dodge omni"], ["360", "ford", "ford escort 2h"], ["331", "dodge", "dodge colt"], ["225", "buick", "buick opel isuzu deluxe"], ["368", "saab", "saab 900s"], ["108", "ford", "ford maverick"], ["34", "dodge", "dodge d200"], ["18", "ford", "ford mustang boss 302"], ["130", "saab", "saab 99le"], ["196", "dodge", "dodge coronet brougham"], ["297", "buick", "buick estate wagon (sw)"], ["231", "dodge", "dodge monaco brougham"], ["174", "ford", "ford mustang ii"], ["258", "dodge", "dodge diplomat"], ["134", "ford", "ford maverick"], ["66", "dodge", "dodge colt hardtop"], ["286", "volkswagen", "volkswagen scirocco"], ["97", "dodge", "dodge coronet custom"], ["154", "dodge", "dodge colt"], ["24", "ford", "ford maverick"], ["198", "ford", "ford gran torino"], ["303", "dodge", "dodge colt hatchback custom"], ["20", "buick", "buick estate wagon (sw)"], ["144", "ford", "ford gran torino"], ["324", "dodge", "dodge aspen"], ["52", "pontiac", "pontiac safari (sw)"], ["240", "ford", "ford thunderbird"], ["10", "amc", "amc ambassador dpl"], ["248", "volkswagen", "volkswagen dasher"], ["355", "datsun", "datsun 210 mpg"], ["21", "toyota", "toyota corona mark ii"], ["149", "audi", "audi fox"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["165", "chevrolet", "chevrolet bel air"], ["204", "chevrolet", "chevrolet woody"], ["178", "pontiac", "pontiac astro"], ["313", "buick", "buick skylark limited"], ["56", "ford", "ford mustang"], ["101", "plymouth", "plymouth fury gran sedan"], ["404", "dodge", "dodge rampage"], ["294", "ford", "ford ltd landau"], ["326", "toyota", "toyota corona liftback"], ["103", "buick", "buick electra 225 custom"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["260", "pontiac", "pontiac phoenix lj"], ["189", "honda", "honda civic cvcc"], ["54", "chevrolet", "chevrolet vega (sw)"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["372", "buick", "buick century"], ["96", "ford", "ford gran torino"], ["381", "pontiac", "pontiac phoenix"], ["397", "chrysler", "chrysler lebaron medallion"], ["239", "chrysler", "chrysler cordoba"], ["375", "chrysler", "chrysler lebaron salon"], ["179", "toyota", "toyota corona"], ["182", "ford", "ford pinto"], ["262", "ford", "ford fairmont (auto)"], ["160", "plymouth", "plymouth valiant custom"], ["215", "volvo", "volvo 245"], ["173", "chevrolet", "chevrolet monza 2+2"], ["73", "ford", "ford galaxie 500"], ["382", "ford", "ford fairmont futura"], ["393", "honda", "honda civic (auto)"], ["340", " volkswagen", "volkswagen rabbit"], ["216", "plymouth", "plymouth volare premier v8"], ["272", "ford", "ford futura"], ["65", "toyota", "toyota corona hardtop"], ["296", "dodge", "dodge st. regis"], ["185", "audi", "audi 100ls"], ["209", "pontiac", "pontiac ventura sj"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["120", "ford", "ford pinto"], ["402", "ford", "ford mustang gl"], ["70", "chevrolet", "chevrolet impala"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:How much does the car accelerate that makes amc hornet sportabout (sw)?	[["13.5"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"], ["16", "renault", "Renault", "3"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["3", "france", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["2", "volkswagen", "Volkswagen", "2"], ["4", "gm", "General Motors", "1"], ["8", "nissan", "Nissan Motors", "4"], ["21", "volvo", "Volvo", "6"], ["14", "opel", "Opel", "2"], ["22", "kia", "Kia Motors", "8"], ["3", "bmw", "BMW", "2"], ["7", "citroen", "Citroen", "3"], ["5", "ford", "Ford Motor Company", "1"], ["11", "honda", "Honda", "4"], ["1", "amc", "American Motor Company", "1"], ["23", "hyundai", "Hyundai", "8"], ["6", "chrysler", "Chrysler", "1"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["2", "germany", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"], ["19", "toyota", "Toyota", "4"], ["15", "peugeaut", "Peugeaut", "3"], ["18", "subaru", "Subaru", "4"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"], ["5", "italy", "2"], ["9", "russia", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["6", "sweden", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["14", "mexico", "1"], ["7", "uk", "2"], ["12", "new zealand", "5"], ["10", "nigeria", "4"], ["4", "japan", "3"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["8", "korea", "3"], ["11", "australia", "5"], ["13", "egypt", "4"], ["1", "usa", "1"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:How many car makers are there in france?	[["3"]]	2,048	Answer:
Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["3", "france", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"], ["15", "peugeaut", "Peugeaut", "3"], ["16", "renault", "Renault", "3"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["2", "germany", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"], ["14", "opel", "Opel", "2"], ["13", "daimler benz", "Daimler Benz", "2"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"], ["15", "brazil", "1"], ["5", "italy", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["2", "volkswagen", "Volkswagen", "2"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["14", "mexico", "1"], ["6", "sweden", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["5", "ford", "Ford Motor Company", "1"], ["7", "citroen", "Citroen", "3"], ["22", "kia", "Kia Motors", "8"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["7", "uk", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["8", "nissan", "Nissan Motors", "4"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["10", "nigeria", "4"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["3", "bmw", "BMW", "2"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["12", "new zealand", "5"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["21", "volvo", "Volvo", "6"], ["11", "honda", "Honda", "4"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["8", "korea", "3"], ["13", "egypt", "4"], ["4", "japan", "3"], ["11", "australia", "5"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"], ["9", "fiat", "Fiat", "5"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["1", "usa", "1"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["6", "chrysler", "Chrysler", "1"], ["19", "toyota", "Toyota", "4"], ["23", "hyundai", "Hyundai", "8"], ["17", "saab", "Saab", "6"], ["18", "subaru", "Subaru", "4"], ["12", "mazda", "Mazda", "4"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the number of makers of care in France?	[["3"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["7", "4", "chevrolet"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["8", "nissan", "Nissan Motors", "4"], ["22", "kia", "Kia Motors", "8"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["8", "6", "chrysler"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"], ["6", "chrysler", "Chrysler", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"], ["20", "8", "nissan"], ["25", "4", "pontiac"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["23", "15", "peugeot"], ["29", "19", "toyota"], ["26", "16", "renault"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["5", "ford", "Ford Motor Company", "1"], ["23", "hyundai", "Hyundai", "8"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["18", "13", "mercedes-benz"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["2", "volkswagen", "Volkswagen", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["13", "5", "ford"], ["22", "14", "opel"], ["34", "23", "hyundai"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["16", "renault", "Renault", "3"], ["19", "toyota", "Toyota", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["35", "6", "jeep"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["7", "citroen", "Citroen", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["15", "11", "honda"], ["21", "4", "oldsmobile"], ["32", "21", "volvo"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["18", "subaru", "Subaru", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["28", "18", "subaru"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["21", "volvo", "Volvo", "6"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["17", "13", "mercedes"], ["9", "7", "citroen"], ["16", "12", "mazda"], ["3", "3", "bmw"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"], ["3", "bmw", "BMW", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["4", "4", "buick"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["11", "honda", "Honda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["5", "4", "cadillac"], ["33", "22", "kia"], ["2", "2", "audi"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"], ["9", "fiat", "Fiat", "5"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["12", "9", "fiat"], ["10", "8", "datsun"], ["24", "6", "plymouth"], ["1", "1", "amc"], ["11", "6", "dodge"], ["27", "17", "saab"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"], ["1", "usa", "1"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"], ["14", "mexico", "1"], ["2", "germany", "2"], ["3", "france", "2"], ["12", "new zealand", "5"], ["4", "japan", "3"], ["8", "korea", "3"], ["7", "uk", "2"], ["11", "australia", "5"], ["6", "sweden", "2"], ["5", "italy", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["19", "5", "mercury"], ["6", "5", "capri"], ["36", "19", "scion"], ["30", "20", "triumph"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["10", "nigeria", "4"], ["13", "egypt", "4"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["14", "10", "hi"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:How many car models are produced in the usa?	[["13"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["8", "6", "chrysler"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"], ["22", "kia", "Kia Motors", "8"], ["8", "nissan", "Nissan Motors", "4"], ["6", "chrysler", "Chrysler", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"], ["7", "4", "chevrolet"], ["23", "15", "peugeot"], ["20", "8", "nissan"], ["18", "13", "mercedes-benz"], ["25", "4", "pontiac"], ["29", "19", "toyota"], ["26", "16", "renault"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["5", "ford", "Ford Motor Company", "1"], ["4", "gm", "General Motors", "1"], ["2", "volkswagen", "Volkswagen", "2"], ["23", "hyundai", "Hyundai", "8"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["22", "14", "opel"], ["13", "5", "ford"], ["17", "13", "mercedes"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["19", "toyota", "Toyota", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["21", "4", "oldsmobile"], ["15", "11", "honda"], ["34", "23", "hyundai"], ["28", "18", "subaru"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["18", "subaru", "Subaru", "4"], ["16", "renault", "Renault", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["35", "6", "jeep"], ["32", "21", "volvo"], ["16", "12", "mazda"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["21", "volvo", "Volvo", "6"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["12", "9", "fiat"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"], ["7", "citroen", "Citroen", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["9", "7", "citroen"], ["3", "3", "bmw"], ["5", "4", "cadillac"], ["4", "4", "buick"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["3", "bmw", "BMW", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["33", "22", "kia"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"], ["12", "mazda", "Mazda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["2", "2", "audi"], ["10", "8", "datsun"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["11", "honda", "Honda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["24", "6", "plymouth"], ["11", "6", "dodge"], ["1", "1", "amc"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"], ["1", "usa", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["27", "17", "saab"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"], ["12", "new zealand", "5"], ["14", "mexico", "1"], ["2", "germany", "2"], ["3", "france", "2"], ["11", "australia", "5"], ["8", "korea", "3"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["19", "5", "mercury"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["5", "italy", "2"], ["4", "japan", "3"], ["7", "uk", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["6", "5", "capri"], ["30", "20", "triumph"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["6", "sweden", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["36", "19", "scion"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["10", "nigeria", "4"], ["13", "egypt", "4"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["14", "10", "hi"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the count of the car models produced in the United States?	[["13"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["4", "16", "8", "304.0", "150", "3433", "12.0", "1970"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["185", "23", "4", "115.0", "95", "2694", "15.0", "1975"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["37", "28", "4", "140.0", "90", "2264", "15.5", "1971"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["350", "30", "4", "135.0", "84", "2385", "12.9", "1981"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["147", "14", "8", "302.0", "140", "4638", "16.0", "1974"], ["150", "26", "4", "79.0", "67", "1963", "15.5", "1974"], ["26", "26", "4", "97.0", "46", "1835", "20.5", "1970"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"], ["54", "22", "4", "140.0", "72", "2408", "19.0", "1971"], ["152", "31", "4", "76.0", "52", "1649", "16.5", "1974"], ["62", "35", "4", "72.0", "69", "1613", "18.0", "1971"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["125", "29", "4", "68.0", "49", "1867", "19.5", "1973"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["138", "26", "4", "122.0", "80", "2451", "16.5", "1974"], ["400", "36", "4", "135.0", "84", "2370", "13.0", "1982"], ["156", "26", "4", "116.0", "75", "2246", "14.0", "1974"], ["69", "21", "4", "122.0", "86", "2226", "16.5", "1972"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["206", "33", "4", "91.0", "53", "1795", "17.4", "1976"], ["238", "15.5", "8", "350.0", "170", "4165", "11.4", "1977"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["384", "36", "4", "105.0", "74", "1980", "15.3", "1982"], ["154", "28", "4", "90.0", "75", "2125", "14.5", "1974"], ["340", "29.8", "4", "89.0", "62", "1845", "15.3", "1980"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["382", "24", "4", "140.0", "92", "2865", "16.4", "1982"], ["146", "14", "8", "318.0", "150", "4457", "13.5", "1974"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["82", "13", "8", "302.0", "140", "4294", "16.0", "1972"], ["304", "27.4", "4", "121.0", "80", "2670", "15.0", "1979"], ["57", "23", "4", "122.0", "86", "2220", "14.0", "1971"], ["190", "28", "4", "107.0", "86", "2464", "15.5", "1976"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["179", "24", "4", "134.0", "96", "2702", "13.5", "1975"], ["38", "25", "4", "113.0", "95", "2228", "14.0", "1971"], ["244", "25.5", "4", "140.0", "89", "2755", "15.8", "1977"], ["59", "30", "4", "79.0", "70", "2074", "19.5", "1971"], ["178", "23", "4", "140.0", "78", "2592", "18.5", "1975"], ["302", "34.1", "4", "86.0", "65", "1975", "15.2", "1979"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["1", "18", "8", "307.0", "130", "3504", "12.0", "1970"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["318", "38.1", "4", "89.0", "60", "1968", "18.8", "1980"], ["49", "14", "8", "318.0", "150", "4096", "13.0", "1971"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["337", "44.6", "4", "91.0", "67", "1850", "13.8", "1980"], ["406", "31", "4", "119.0", "82", "2720", "19.4", "1982"], ["241", "29", "4", "97.0", "78", "1940", "14.5", "1977"], ["390", "36", "4", "107.0", "75", "2205", "14.5", "1982"], ["397", "26", "4", "156.0", "92", "2585", "14.5", "1982"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["180", "25", "4", "90.0", "71", "2223", "16.5", "1975"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the average miles per gallon(mpg) of the cars with 4 cylinders?	[["28.86231884057971"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["185", "23", "4", "115.0", "95", "2694", "15.0", "1975"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["4", "16", "8", "304.0", "150", "3433", "12.0", "1970"], ["147", "14", "8", "302.0", "140", "4638", "16.0", "1974"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["146", "14", "8", "318.0", "150", "4457", "13.5", "1974"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["400", "36", "4", "135.0", "84", "2370", "13.0", "1982"], ["37", "28", "4", "140.0", "90", "2264", "15.5", "1971"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["156", "26", "4", "116.0", "75", "2246", "14.0", "1974"], ["350", "30", "4", "135.0", "84", "2385", "12.9", "1981"], ["138", "26", "4", "122.0", "80", "2451", "16.5", "1974"], ["54", "22", "4", "140.0", "72", "2408", "19.0", "1971"], ["190", "28", "4", "107.0", "86", "2464", "15.5", "1976"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["150", "26", "4", "79.0", "67", "1963", "15.5", "1974"], ["154", "28", "4", "90.0", "75", "2125", "14.5", "1974"], ["164", "16", "8", "400.0", "170", "4668", "11.5", "1975"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["82", "13", "8", "302.0", "140", "4294", "16.0", "1972"], ["77", "12", "8", "350.0", "160", "4456", "13.5", "1972"], ["69", "21", "4", "122.0", "86", "2226", "16.5", "1972"], ["390", "36", "4", "107.0", "75", "2205", "14.5", "1982"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["148", "14", "8", "304.0", "150", "4257", "15.5", "1974"], ["179", "24", "4", "134.0", "96", "2702", "13.5", "1975"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["238", "15.5", "8", "350.0", "170", "4165", "11.4", "1977"], ["192", "25", "4", "140.0", "92", "2572", "14.9", "1976"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["52", "13", "8", "400.0", "175", "5140", "12.0", "1971"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["382", "24", "4", "140.0", "92", "2865", "16.4", "1982"], ["166", "16", "8", "318.0", "150", "4498", "14.5", "1975"], ["125", "29", "4", "68.0", "49", "1867", "19.5", "1973"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["26", "26", "4", "97.0", "46", "1835", "20.5", "1970"], ["78", "13", "8", "400.0", "190", "4422", "12.5", "1972"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["62", "35", "4", "72.0", "69", "1613", "18.0", "1971"], ["84", "18", "4", "121.0", "112", "2933", "14.5", "1972"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["152", "31", "4", "76.0", "52", "1649", "16.5", "1974"], ["178", "23", "4", "140.0", "78", "2592", "18.5", "1975"], ["49", "14", "8", "318.0", "150", "4096", "13.0", "1971"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["57", "23", "4", "122.0", "86", "2220", "14.0", "1971"], ["397", "26", "4", "156.0", "92", "2585", "14.5", "1982"], ["38", "25", "4", "113.0", "95", "2228", "14.0", "1971"], ["51", "13", "8", "400.0", "170", "4746", "12.0", "1971"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["74", "17", "8", "304.0", "150", "3672", "11.5", "1972"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the average miles per gallon of all the cards with 4 cylinders?	[["28.86231884057971"]]	2,048	Answer:
Table cars_data: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["146", "14", "8", "318.0", "150", "4457", "13.5", "1974"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["147", "14", "8", "302.0", "140", "4638", "16.0", "1974"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["8", "14", "8", "440.0", "215", "4312", "8.5", "1970"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["18", "null", "8", "302.0", "140", "3353", "8.0", "1970"], ["124", "16", "8", "400.0", "230", "4278", "9.5", "1973"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["154", "28", "4", "90.0", "75", "2125", "14.5", "1974"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["35", "9", "8", "304.0", "193", "4732", "18.5", "1970"], ["77", "12", "8", "350.0", "160", "4456", "13.5", "1972"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["78", "13", "8", "400.0", "190", "4422", "12.5", "1972"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["74", "17", "8", "304.0", "150", "3672", "11.5", "1972"], ["138", "26", "4", "122.0", "80", "2451", "16.5", "1974"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["143", "18", "6", "225.0", "105", "3613", "16.5", "1974"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["111", "11", "8", "400.0", "150", "4997", "14.0", "1973"], ["4", "16", "8", "304.0", "150", "3433", "12.0", "1970"], ["88", "22", "4", "122.0", "86", "2395", "16.0", "1972"], ["113", "13", "8", "360.0", "170", "4654", "13.0", "1973"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["98", "12", "8", "429.0", "198", "4952", "11.5", "1973"], ["49", "14", "8", "318.0", "150", "4096", "13.0", "1971"], ["148", "14", "8", "304.0", "150", "4257", "15.5", "1974"], ["12", "null", "8", "350.0", "165", "4142", "11.5", "1970"], ["81", "13", "8", "307.0", "130", "4098", "14.0", "1972"], ["164", "16", "8", "400.0", "170", "4668", "11.5", "1975"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["94", "14", "8", "304.0", "150", "3672", "11.5", "1973"], ["97", "15", "8", "318.0", "150", "3777", "12.5", "1973"], ["125", "29", "4", "68.0", "49", "1867", "19.5", "1973"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["139", "32", "4", "71.0", "65", "1836", "21.0", "1974"], ["73", "14", "8", "351.0", "153", "4129", "13.0", "1972"], ["82", "13", "8", "302.0", "140", "4294", "16.0", "1972"], ["112", "12", "8", "400.0", "167", "4906", "12.5", "1973"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["156", "26", "4", "116.0", "75", "2246", "14.0", "1974"], ["1", "18", "8", "307.0", "130", "3504", "12.0", "1970"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["96", "14", "8", "302.0", "137", "4042", "14.5", "1973"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["166", "16", "8", "318.0", "150", "4498", "14.5", "1975"], ["75", "11", "8", "429.0", "208", "4633", "11.0", "1972"], ["16", "15", "8", "383.0", "170", "3563", "10.0", "1970"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["133", "20", "6", "198.0", "95", "3102", "16.5", "1974"], ["47", "14", "8", "400.0", "175", "4464", "11.5", "1971"], ["387", "38", "4", "105.0", "63", "2125", "14.7", "1982"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["84", "18", "4", "121.0", "112", "2933", "14.5", "1972"], ["238", "15.5", "8", "350.0", "170", "4165", "11.4", "1977"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the smallest weight of the car produced with 8 cylinders on 1974 ?	[["4141"]]	2,048	Answer:
Table cars_data: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["146", "14", "8", "318.0", "150", "4457", "13.5", "1974"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["147", "14", "8", "302.0", "140", "4638", "16.0", "1974"], ["8", "14", "8", "440.0", "215", "4312", "8.5", "1970"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["124", "16", "8", "400.0", "230", "4278", "9.5", "1973"], ["77", "12", "8", "350.0", "160", "4456", "13.5", "1972"], ["78", "13", "8", "400.0", "190", "4422", "12.5", "1972"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["111", "11", "8", "400.0", "150", "4997", "14.0", "1973"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["154", "28", "4", "90.0", "75", "2125", "14.5", "1974"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["138", "26", "4", "122.0", "80", "2451", "16.5", "1974"], ["164", "16", "8", "400.0", "170", "4668", "11.5", "1975"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["49", "14", "8", "318.0", "150", "4096", "13.0", "1971"], ["112", "12", "8", "400.0", "167", "4906", "12.5", "1973"], ["35", "9", "8", "304.0", "193", "4732", "18.5", "1970"], ["113", "13", "8", "360.0", "170", "4654", "13.0", "1973"], ["74", "17", "8", "304.0", "150", "3672", "11.5", "1972"], ["81", "13", "8", "307.0", "130", "4098", "14.0", "1972"], ["98", "12", "8", "429.0", "198", "4952", "11.5", "1973"], ["88", "22", "4", "122.0", "86", "2395", "16.0", "1972"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["148", "14", "8", "304.0", "150", "4257", "15.5", "1974"], ["82", "13", "8", "302.0", "140", "4294", "16.0", "1972"], ["108", "18", "6", "250.0", "88", "3021", "16.5", "1973"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["4", "16", "8", "304.0", "150", "3433", "12.0", "1970"], ["156", "26", "4", "116.0", "75", "2246", "14.0", "1974"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["94", "14", "8", "304.0", "150", "3672", "11.5", "1973"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["143", "18", "6", "225.0", "105", "3613", "16.5", "1974"], ["97", "15", "8", "318.0", "150", "3777", "12.5", "1973"], ["73", "14", "8", "351.0", "153", "4129", "13.0", "1972"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["47", "14", "8", "400.0", "175", "4464", "11.5", "1971"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["51", "13", "8", "400.0", "170", "4746", "12.0", "1971"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["139", "32", "4", "71.0", "65", "1836", "21.0", "1974"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["166", "16", "8", "318.0", "150", "4498", "14.5", "1975"], ["125", "29", "4", "68.0", "49", "1867", "19.5", "1973"], ["18", "null", "8", "302.0", "140", "3353", "8.0", "1970"], ["96", "14", "8", "302.0", "137", "4042", "14.5", "1973"], ["71", "14", "8", "400.0", "175", "4385", "12.0", "1972"], ["136", "15", "6", "250.0", "100", "3336", "17.0", "1974"], ["135", "19", "6", "232.0", "100", "2901", "16.0", "1974"], ["142", "16", "6", "258.0", "110", "3632", "18.0", "1974"], ["84", "18", "4", "121.0", "112", "2933", "14.5", "1972"], ["196", "16", "8", "318.0", "150", "4190", "13.0", "1976"], ["75", "11", "8", "429.0", "208", "4633", "11.0", "1972"], ["121", "21", "6", "155.0", "107", "2472", "14.0", "1973"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the minimum weight of the car with 8 cylinders produced in 1974 ?	[["4141"]]	2,048	Answer:
Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"], ["23", "15", "peugeot"], ["26", "16", "renault"], ["22", "14", "opel"], ["32", "21", "volvo"], ["29", "19", "toyota"], ["7", "4", "chevrolet"], ["20", "8", "nissan"], ["15", "11", "honda"], ["25", "4", "pontiac"], ["2", "2", "audi"], ["3", "3", "bmw"], ["28", "18", "subaru"], ["21", "4", "oldsmobile"], ["34", "23", "hyundai"], ["33", "22", "kia"], ["9", "7", "citroen"], ["8", "6", "chrysler"], ["27", "17", "saab"], ["35", "6", "jeep"], ["10", "8", "datsun"], ["17", "13", "mercedes"], ["24", "6", "plymouth"], ["18", "13", "mercedes-benz"], ["1", "1", "amc"], ["12", "9", "fiat"], ["16", "12", "mazda"], ["36", "19", "scion"], ["5", "4", "cadillac"], ["6", "5", "capri"], ["11", "6", "dodge"], ["13", "5", "ford"], ["4", "4", "buick"], ["19", "5", "mercury"], ["30", "20", "triumph"], ["14", "10", "hi"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are all the makers and models?	[["1", "amc"], ["2", "audi"], ["3", "bmw"], ["4", "buick"], ["4", "cadillac"], ["5", "capri"], ["4", "chevrolet"], ["6", "chrysler"], ["7", "citroen"], ["8", "datsun"], ["6", "dodge"], ["9", "fiat"], ["5", "ford"], ["10", "hi"], ["11", "honda"], ["12", "mazda"], ["13", "mercedes"], ["13", "mercedes-benz"], ["5", "mercury"], ["8", "nissan"], ["4", "oldsmobile"], ["14", "opel"], ["15", "peugeot"], ["6", "plymouth"], ["4", "pontiac"], ["16", "renault"], ["17", "saab"], ["18", "subaru"], ["19", "toyota"], ["20", "triumph"], ["2", "volkswagen"], ["21", "volvo"], ["22", "kia"], ["23", "hyundai"], ["6", "jeep"], ["19", "scion"]]	2,048	Answer:
Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"], ["26", "16", "renault"], ["23", "15", "peugeot"], ["22", "14", "opel"], ["32", "21", "volvo"], ["2", "2", "audi"], ["20", "8", "nissan"], ["25", "4", "pontiac"], ["15", "11", "honda"], ["29", "19", "toyota"], ["7", "4", "chevrolet"], ["3", "3", "bmw"], ["21", "4", "oldsmobile"], ["28", "18", "subaru"], ["33", "22", "kia"], ["27", "17", "saab"], ["17", "13", "mercedes"], ["34", "23", "hyundai"], ["24", "6", "plymouth"], ["1", "1", "amc"], ["9", "7", "citroen"], ["8", "6", "chrysler"], ["18", "13", "mercedes-benz"], ["10", "8", "datsun"], ["35", "6", "jeep"], ["36", "19", "scion"], ["12", "9", "fiat"], ["6", "5", "capri"], ["5", "4", "cadillac"], ["16", "12", "mazda"], ["11", "6", "dodge"], ["13", "5", "ford"], ["19", "5", "mercury"], ["4", "4", "buick"], ["30", "20", "triumph"], ["14", "10", "hi"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the makers and models?	[["1", "amc"], ["2", "audi"], ["3", "bmw"], ["4", "buick"], ["4", "cadillac"], ["5", "capri"], ["4", "chevrolet"], ["6", "chrysler"], ["7", "citroen"], ["8", "datsun"], ["6", "dodge"], ["9", "fiat"], ["5", "ford"], ["10", "hi"], ["11", "honda"], ["12", "mazda"], ["13", "mercedes"], ["13", "mercedes-benz"], ["5", "mercury"], ["8", "nissan"], ["4", "oldsmobile"], ["14", "opel"], ["15", "peugeot"], ["6", "plymouth"], ["4", "pontiac"], ["16", "renault"], ["17", "saab"], ["18", "subaru"], ["19", "toyota"], ["20", "triumph"], ["2", "volkswagen"], ["21", "volvo"], ["22", "kia"], ["23", "hyundai"], ["6", "jeep"], ["19", "scion"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["8", "nissan", "Nissan Motors", "4"], ["13", "daimler benz", "Daimler Benz", "2"], ["1", "amc", "American Motor Company", "1"], ["4", "gm", "General Motors", "1"], ["16", "renault", "Renault", "3"], ["22", "kia", "Kia Motors", "8"], ["21", "volvo", "Volvo", "6"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["2", "volkswagen", "Volkswagen", "2"], ["19", "toyota", "Toyota", "4"], ["5", "ford", "Ford Motor Company", "1"], ["23", "hyundai", "Hyundai", "8"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["2", "germany", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"], ["7", "citroen", "Citroen", "3"], ["6", "chrysler", "Chrysler", "1"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["11", "honda", "Honda", "4"], ["18", "subaru", "Subaru", "4"], ["9", "fiat", "Fiat", "5"], ["3", "bmw", "BMW", "2"], ["12", "mazda", "Mazda", "4"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["3", "france", "2"], ["14", "mexico", "1"], ["5", "italy", "2"], ["6", "sweden", "2"], ["8", "korea", "3"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["4", "japan", "3"], ["1", "usa", "1"], ["11", "australia", "5"], ["12", "new zealand", "5"], ["10", "nigeria", "4"], ["7", "uk", "2"], ["13", "egypt", "4"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"], ["20", "triumph", "Triumph", "7"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the countries having at least one car maker? List name and id.	[["usa", "1"], ["germany", "2"], ["france", "3"], ["japan", "4"], ["italy", "5"], ["sweden", "6"], ["uk", "7"], ["korea", "8"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"], ["8", "nissan", "Nissan Motors", "4"], ["16", "renault", "Renault", "3"], ["22", "kia", "Kia Motors", "8"], ["4", "gm", "General Motors", "1"], ["1", "amc", "American Motor Company", "1"], ["21", "volvo", "Volvo", "6"], ["2", "volkswagen", "Volkswagen", "2"], ["23", "hyundai", "Hyundai", "8"], ["19", "toyota", "Toyota", "4"], ["5", "ford", "Ford Motor Company", "1"], ["3", "bmw", "BMW", "2"], ["6", "chrysler", "Chrysler", "1"], ["18", "subaru", "Subaru", "4"], ["7", "citroen", "Citroen", "3"], ["12", "mazda", "Mazda", "4"], ["14", "opel", "Opel", "2"], ["9", "fiat", "Fiat", "5"], ["11", "honda", "Honda", "4"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["2", "germany", "2"], ["15", "brazil", "1"], ["9", "russia", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["3", "france", "2"], ["8", "korea", "3"], ["6", "sweden", "2"], ["14", "mexico", "1"], ["4", "japan", "3"], ["5", "italy", "2"], ["10", "nigeria", "4"], ["1", "usa", "1"], ["7", "uk", "2"], ["12", "new zealand", "5"], ["11", "australia", "5"], ["13", "egypt", "4"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"], ["20", "triumph", "Triumph", "7"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the names and ids of all countries with at least one car maker?	[["usa", "1"], ["germany", "2"], ["france", "3"], ["japan", "4"], ["italy", "5"], ["sweden", "6"], ["uk", "7"], ["korea", "8"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["49", "14", "8", "318.0", "150", "4096", "13.0", "1971"], ["148", "14", "8", "304.0", "150", "4257", "15.5", "1974"], ["51", "13", "8", "400.0", "170", "4746", "12.0", "1971"], ["150", "26", "4", "79.0", "67", "1963", "15.5", "1974"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["216", "13", "8", "318.0", "150", "3940", "13.2", "1976"], ["136", "15", "6", "250.0", "100", "3336", "17.0", "1974"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["223", "13", "8", "318.0", "150", "3755", "14.0", "1976"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["47", "14", "8", "400.0", "175", "4464", "11.5", "1971"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["146", "14", "8", "318.0", "150", "4457", "13.5", "1974"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["19", "15", "8", "400.0", "150", "3761", "9.5", "1970"], ["147", "14", "8", "302.0", "140", "4638", "16.0", "1974"], ["33", "10", "8", "307.0", "200", "4376", "15.0", "1970"], ["52", "13", "8", "400.0", "175", "5140", "12.0", "1971"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["73", "14", "8", "351.0", "153", "4129", "13.0", "1972"], ["37", "28", "4", "140.0", "90", "2264", "15.5", "1971"], ["111", "11", "8", "400.0", "150", "4997", "14.0", "1973"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["300", "18.5", "8", "360.0", "150", "3940", "13.0", "1979"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["57", "23", "4", "122.0", "86", "2220", "14.0", "1971"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["402", "27", "4", "140.0", "86", "2790", "15.6", "1982"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["43", "17", "6", "250.0", "100", "3329", "15.5", "1971"], ["301", "31.9", "4", "89.0", "71", "1925", "14.0", "1979"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["100", "13", "8", "351.0", "158", "4363", "13.0", "1973"], ["238", "15.5", "8", "350.0", "170", "4165", "11.4", "1977"], ["80", "15", "8", "304.0", "150", "3892", "12.5", "1972"], ["4", "16", "8", "304.0", "150", "3433", "12.0", "1970"], ["54", "22", "4", "140.0", "72", "2408", "19.0", "1971"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["270", "19.2", "8", "305.0", "145", "3425", "13.2", "1978"], ["82", "13", "8", "302.0", "140", "4294", "16.0", "1972"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["113", "13", "8", "360.0", "170", "4654", "13.0", "1973"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["48", "14", "8", "351.0", "153", "4154", "13.5", "1971"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["71", "14", "8", "400.0", "175", "4385", "12.0", "1972"], ["195", "17.5", "8", "305.0", "140", "4215", "13.0", "1976"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["396", "38", "6", "262.0", "85", "3015", "17.0", "1982"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["10", "15", "8", "390.0", "190", "3850", "8.5", "1970"], ["398", "22", "6", "232.0", "112", "2835", "14.7", "1982"], ["399", "32", "4", "144.0", "96", "2665", "13.9", "1982"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["135", "19", "6", "232.0", "100", "2901", "16.0", "1974"], ["17", "14", "8", "340.0", "160", "3609", "8.0", "1970"], ["232", "15", "8", "302.0", "130", "4295", "14.9", "1977"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the number of the cars with horsepower more than 150?	[["281"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["49", "14", "8", "318.0", "150", "4096", "13.0", "1971"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["51", "13", "8", "400.0", "170", "4746", "12.0", "1971"], ["148", "14", "8", "304.0", "150", "4257", "15.5", "1974"], ["150", "26", "4", "79.0", "67", "1963", "15.5", "1974"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["223", "13", "8", "318.0", "150", "3755", "14.0", "1976"], ["216", "13", "8", "318.0", "150", "3940", "13.2", "1976"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["136", "15", "6", "250.0", "100", "3336", "17.0", "1974"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["47", "14", "8", "400.0", "175", "4464", "11.5", "1971"], ["19", "15", "8", "400.0", "150", "3761", "9.5", "1970"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["146", "14", "8", "318.0", "150", "4457", "13.5", "1974"], ["33", "10", "8", "307.0", "200", "4376", "15.0", "1970"], ["52", "13", "8", "400.0", "175", "5140", "12.0", "1971"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["37", "28", "4", "140.0", "90", "2264", "15.5", "1971"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["111", "11", "8", "400.0", "150", "4997", "14.0", "1973"], ["147", "14", "8", "302.0", "140", "4638", "16.0", "1974"], ["300", "18.5", "8", "360.0", "150", "3940", "13.0", "1979"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["57", "23", "4", "122.0", "86", "2220", "14.0", "1971"], ["73", "14", "8", "351.0", "153", "4129", "13.0", "1972"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["238", "15.5", "8", "350.0", "170", "4165", "11.4", "1977"], ["402", "27", "4", "140.0", "86", "2790", "15.6", "1982"], ["4", "16", "8", "304.0", "150", "3433", "12.0", "1970"], ["80", "15", "8", "304.0", "150", "3892", "12.5", "1972"], ["43", "17", "6", "250.0", "100", "3329", "15.5", "1971"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["113", "13", "8", "360.0", "170", "4654", "13.0", "1973"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["54", "22", "4", "140.0", "72", "2408", "19.0", "1971"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["270", "19.2", "8", "305.0", "145", "3425", "13.2", "1978"], ["82", "13", "8", "302.0", "140", "4294", "16.0", "1972"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["301", "31.9", "4", "89.0", "71", "1925", "14.0", "1979"], ["100", "13", "8", "351.0", "158", "4363", "13.0", "1973"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["10", "15", "8", "390.0", "190", "3850", "8.5", "1970"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["71", "14", "8", "400.0", "175", "4385", "12.0", "1972"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["48", "14", "8", "351.0", "153", "4154", "13.5", "1971"], ["195", "17.5", "8", "305.0", "140", "4215", "13.0", "1976"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"], ["30", "26", "4", "121.0", "113", "2234", "12.5", "1970"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["42", "16", "6", "225.0", "105", "3439", "15.5", "1971"], ["17", "14", "8", "340.0", "160", "3609", "8.0", "1970"], ["396", "38", "6", "262.0", "85", "3015", "17.0", "1982"], ["399", "32", "4", "144.0", "96", "2665", "13.9", "1982"], ["135", "19", "6", "232.0", "100", "2901", "16.0", "1974"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the number of cars with a horsepower greater than 150?	[["281"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["163", "15", "6", "250.0", "72", "3158", "19.5", "1975"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["1", "18", "8", "307.0", "130", "3504", "12.0", "1970"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["171", "18", "6", "225.0", "95", "3785", "19.0", "1975"], ["164", "16", "8", "400.0", "170", "4668", "11.5", "1975"], ["350", "30", "4", "135.0", "84", "2385", "12.9", "1981"], ["169", "16", "6", "250.0", "105", "3897", "18.5", "1975"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["200", "22", "6", "250.0", "105", "3353", "14.5", "1976"], ["170", "15", "6", "258.0", "110", "3730", "19.0", "1975"], ["97", "15", "8", "318.0", "150", "3777", "12.5", "1973"], ["161", "18", "6", "250.0", "105", "3459", "16.0", "1975"], ["112", "12", "8", "400.0", "167", "4906", "12.5", "1973"], ["35", "9", "8", "304.0", "193", "4732", "18.5", "1970"], ["221", "13", "8", "350.0", "145", "4055", "12.0", "1976"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["238", "15.5", "8", "350.0", "170", "4165", "11.4", "1977"], ["178", "23", "4", "140.0", "78", "2592", "18.5", "1975"], ["185", "23", "4", "115.0", "95", "2694", "15.0", "1975"], ["61", "31", "4", "71.0", "65", "1773", "19.0", "1971"], ["49", "14", "8", "318.0", "150", "4096", "13.0", "1971"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["34", "11", "8", "318.0", "210", "4382", "13.5", "1970"], ["186", "23", "4", "120.0", "88", "2957", "17.0", "1975"], ["143", "18", "6", "225.0", "105", "3613", "16.5", "1974"], ["181", "24", "4", "119.0", "97", "2545", "17.0", "1975"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["179", "24", "4", "134.0", "96", "2702", "13.5", "1975"], ["47", "14", "8", "400.0", "175", "4464", "11.5", "1971"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["108", "18", "6", "250.0", "88", "3021", "16.5", "1973"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["51", "13", "8", "400.0", "170", "4746", "12.0", "1971"], ["43", "17", "6", "250.0", "100", "3329", "15.5", "1971"], ["62", "35", "4", "72.0", "69", "1613", "18.0", "1971"], ["195", "17.5", "8", "305.0", "140", "4215", "13.0", "1976"], ["180", "25", "4", "90.0", "71", "2223", "16.5", "1975"], ["53", "18", "6", "258.0", "110", "2962", "13.5", "1971"], ["273", "17.5", "8", "318.0", "140", "4080", "13.7", "1978"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["166", "16", "8", "318.0", "150", "4498", "14.5", "1975"], ["52", "13", "8", "400.0", "175", "5140", "12.0", "1971"], ["381", "27", "4", "151.0", "90", "2735", "18.0", "1982"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["77", "12", "8", "350.0", "160", "4456", "13.5", "1972"], ["119", "18", "3", "70.0", "90", "2124", "13.5", "1973"], ["33", "10", "8", "307.0", "200", "4376", "15.0", "1970"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["57", "23", "4", "122.0", "86", "2220", "14.0", "1971"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the average weight of cars each year?	[["3441.3142857142857", "1970"], ["2960.344827586207", "1971"], ["3237.714285714286", "1972"], ["3419.025", "1973"], ["2877.925925925926", "1974"], ["3176.8", "1975"], ["3078.735294117647", "1976"], ["2997.3571428571427", "1977"], ["2861.8055555555557", "1978"], ["3055.344827586207", "1979"], ["2436.655172413793", "1980"], ["2532.1666666666665", "1981"], ["2453.548387096774", "1982"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["163", "15", "6", "250.0", "72", "3158", "19.5", "1975"], ["171", "18", "6", "225.0", "95", "3785", "19.0", "1975"], ["1", "18", "8", "307.0", "130", "3504", "12.0", "1970"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["164", "16", "8", "400.0", "170", "4668", "11.5", "1975"], ["161", "18", "6", "250.0", "105", "3459", "16.0", "1975"], ["170", "15", "6", "258.0", "110", "3730", "19.0", "1975"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["169", "16", "6", "250.0", "105", "3897", "18.5", "1975"], ["178", "23", "4", "140.0", "78", "2592", "18.5", "1975"], ["185", "23", "4", "115.0", "95", "2694", "15.0", "1975"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["365", "32.9", "4", "119.0", "100", "2615", "14.8", "1981"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["166", "16", "8", "318.0", "150", "4498", "14.5", "1975"], ["261", "20.5", "6", "200.0", "95", "3155", "18.2", "1978"], ["350", "30", "4", "135.0", "84", "2385", "12.9", "1981"], ["181", "24", "4", "119.0", "97", "2545", "17.0", "1975"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["182", "18", "6", "171.0", "97", "2984", "14.5", "1975"], ["235", "19", "6", "225.0", "100", "3630", "17.7", "1977"], ["179", "24", "4", "134.0", "96", "2702", "13.5", "1975"], ["97", "15", "8", "318.0", "150", "3777", "12.5", "1973"], ["34", "11", "8", "318.0", "210", "4382", "13.5", "1970"], ["173", "20", "8", "262.0", "110", "3221", "13.5", "1975"], ["184", "19", "6", "232.0", "90", "3211", "17.0", "1975"], ["180", "25", "4", "90.0", "71", "2223", "16.5", "1975"], ["112", "12", "8", "400.0", "167", "4906", "12.5", "1973"], ["35", "9", "8", "304.0", "193", "4732", "18.5", "1970"], ["61", "31", "4", "71.0", "65", "1773", "19.0", "1971"], ["226", "36", "4", "79.0", "58", "1825", "18.6", "1977"], ["215", "20", "4", "130.0", "102", "3150", "15.7", "1976"], ["231", "15.5", "8", "318.0", "145", "4140", "13.7", "1977"], ["381", "27", "4", "151.0", "90", "2735", "18.0", "1982"], ["273", "17.5", "8", "318.0", "140", "4080", "13.7", "1978"], ["115", "18", "6", "232.0", "100", "2789", "15.0", "1973"], ["283", "17", "6", "163.0", "125", "3140", "13.6", "1978"], ["186", "23", "4", "120.0", "88", "2957", "17.0", "1975"], ["160", "19", "6", "225.0", "95", "3264", "16.0", "1975"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["223", "13", "8", "318.0", "150", "3755", "14.0", "1976"], ["174", "13", "8", "302.0", "129", "3169", "12.0", "1975"], ["176", "23", "4", "140.0", "83", "2639", "17.0", "1975"], ["221", "13", "8", "350.0", "145", "4055", "12.0", "1976"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["103", "12", "8", "455.0", "225", "4951", "11.0", "1973"], ["105", "18", "6", "225.0", "105", "3121", "16.5", "1973"], ["108", "18", "6", "250.0", "88", "3021", "16.5", "1973"], ["53", "18", "6", "258.0", "110", "2962", "13.5", "1971"], ["230", "17", "8", "260.0", "110", "4060", "19.0", "1977"], ["177", "20", "6", "232.0", "100", "2914", "16.0", "1975"], ["200", "22", "6", "250.0", "105", "3353", "14.5", "1976"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["26", "26", "4", "97.0", "46", "1835", "20.5", "1970"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["236", "18.5", "6", "250.0", "98", "3525", "19.0", "1977"], ["196", "16", "8", "318.0", "150", "4190", "13.0", "1976"], ["135", "19", "6", "232.0", "100", "2901", "16.0", "1974"], ["106", "16", "6", "250.0", "100", "3278", "18.0", "1973"], ["245", "30.5", "4", "98.0", "63", "2051", "17.0", "1977"], ["41", "19", "6", "232.0", "100", "2634", "13.0", "1971"], ["143", "18", "6", "225.0", "105", "3613", "16.5", "1974"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the average weight and year for each year?	[["3441.3142857142857", "1970"], ["2960.344827586207", "1971"], ["3237.714285714286", "1972"], ["3419.025", "1973"], ["2877.925925925926", "1974"], ["3176.8", "1975"], ["3078.735294117647", "1976"], ["2997.3571428571427", "1977"], ["2861.8055555555557", "1978"], ["3055.344827586207", "1979"], ["2436.655172413793", "1980"], ["2532.1666666666665", "1981"], ["2453.548387096774", "1982"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"], ["16", "renault", "Renault", "3"], ["8", "nissan", "Nissan Motors", "4"], ["4", "gm", "General Motors", "1"], ["3", "bmw", "BMW", "2"], ["2", "volkswagen", "Volkswagen", "2"], ["7", "citroen", "Citroen", "3"], ["22", "kia", "Kia Motors", "8"], ["21", "volvo", "Volvo", "6"]]Table CONTINENTS: [["ContId", "Continent"], ["2", "europe"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["5", "ford", "Ford Motor Company", "1"], ["14", "opel", "Opel", "2"], ["23", "hyundai", "Hyundai", "8"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["3", "france", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["19", "toyota", "Toyota", "4"], ["1", "amc", "American Motor Company", "1"], ["12", "mazda", "Mazda", "4"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["2", "germany", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"], ["11", "honda", "Honda", "4"], ["6", "chrysler", "Chrysler", "1"]]Table CONTINENTS: [["ContId", "Continent"], ["3", "asia"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["18", "subaru", "Subaru", "4"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"], ["4", "japan", "3"], ["5", "italy", "2"], ["15", "brazil", "1"], ["6", "sweden", "2"], ["8", "korea", "3"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"], ["15", "peugeaut", "Peugeaut", "3"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["7", "uk", "2"], ["14", "mexico", "1"]]Table CONTINENTS: [["ContId", "Continent"], ["4", "africa"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["10", "nigeria", "4"]]Table CONTINENTS: [["ContId", "Continent"], ["1", "america"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["12", "new zealand", "5"], ["11", "australia", "5"], ["13", "egypt", "4"], ["1", "usa", "1"]]Table CONTINENTS: [["ContId", "Continent"], ["5", "australia"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Which countries in europe have at least 3 car manufacturers?	[["france"], ["germany"]]	2,048	Answer:
Table CONTINENTS: [["ContId", "Continent"], ["2", "europe"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["16", "renault", "Renault", "3"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["3", "france", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"], ["13", "daimler benz", "Daimler Benz", "2"], ["7", "citroen", "Citroen", "3"], ["3", "bmw", "BMW", "2"], ["14", "opel", "Opel", "2"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["2", "germany", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["8", "nissan", "Nissan Motors", "4"], ["2", "volkswagen", "Volkswagen", "2"], ["21", "volvo", "Volvo", "6"]]Table CONTINENTS: [["ContId", "Continent"], ["3", "asia"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["22", "kia", "Kia Motors", "8"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["4", "japan", "3"], ["9", "russia", "2"], ["8", "korea", "3"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["5", "italy", "2"], ["6", "sweden", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"], ["5", "ford", "Ford Motor Company", "1"], ["23", "hyundai", "Hyundai", "8"], ["19", "toyota", "Toyota", "4"], ["9", "fiat", "Fiat", "5"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"], ["11", "honda", "Honda", "4"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["7", "uk", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"], ["6", "chrysler", "Chrysler", "1"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["14", "mexico", "1"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["18", "subaru", "Subaru", "4"]]Table CONTINENTS: [["ContId", "Continent"], ["4", "africa"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["10", "nigeria", "4"], ["1", "usa", "1"], ["11", "australia", "5"], ["12", "new zealand", "5"], ["13", "egypt", "4"]]Table CONTINENTS: [["ContId", "Continent"], ["1", "america"], ["5", "australia"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the names of all European countries with at least 3 manufacturers?	[["france"], ["germany"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["285", "peugeot", "peugeot 604sl"], ["59", "peugeot", "peugeot 304"], ["147", "ford", "ford gran torino (sw)"], ["340", " volkswagen", "volkswagen rabbit"], ["27", "peugeot", "peugeot 504"], ["82", "ford", "ford gran torino (sw)"], ["87", "renault", "renault 12 (sw)"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["217", "peugeot", "peugeot 504"], ["216", "plymouth", "plymouth volare premier v8"], ["355", "datsun", "datsun 210 mpg"], ["67", "volkswagen", "volkswagen type 3"], ["338", "renault", "renault lecar deluxe"], ["344", "ford", "ford mustang cobra"], ["144", "ford", "ford gran torino"], ["211", "volkswagen", "volkswagen rabbit"], ["194", "renault", "renault 12tl"], ["273", "dodge", "dodge magnum xe"], ["288", "pontiac", "pontiac lemans v6"], ["173", "chevrolet", "chevrolet monza 2+2"], ["334", "volkswagen", "vw dasher (diesel)"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["229", "chevrolet", "chevrolet caprice classic"], ["241", "volkswagen", "volkswagen rabbit custom"], ["50", "dodge", "dodge monaco (sw)"], ["363", "honda", "honda prelude"], ["44", "ford", "ford torino 500"], ["186", "peugeot", "peugeot 504"], ["226", "renault", "renault 5 gtl"], ["198", "ford", "ford gran torino"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["307", "peugeot", "peugeot 504"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["293", "chevrolet", "chevrolet caprice classic"], ["283", "volvo", "volvo 264gl"], ["183", "volkswagen", "volkswagen rabbit"], ["110", "volkswagen", "volkswagen super beetle"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["96", "ford", "ford gran torino"], ["362", "renault", "renault 18i"], ["49", "plymouth", "plymouth fury iii"], ["176", "ford", "ford pinto"], ["72", "plymouth", "plymouth fury iii"], ["335", "audi", "audi 5000s (diesel)"], ["215", "volvo", "volvo 245"], ["220", "cadillac", "cadillac seville"], ["205", "volkswagen", "vw rabbit"], ["187", "volvo", "volvo 244dl"], ["239", "chrysler", "chrysler cordoba"], ["317", "volkswagen", "vw rabbit"], ["182", "ford", "ford pinto"], ["18", "ford", "ford mustang boss 302"], ["180", "volkswagen", "volkswagen dasher"], ["8", "plymouth", "plymouth fury iii"], ["126", "opel", "opel manta"], ["371", "datsun", "datsun 810 maxima"], ["13", "ford", "ford torino (sw)"], ["40", "volkswagen", "volkswagen super beetle 117"], ["86", "peugeot", "peugeot 504 (sw)"], ["384", "volkswagen", "volkswagen rabbit l"], ["319", "chevrolet", "chevrolet chevette"], ["48", "ford", "ford galaxie 500"], ["120", "ford", "ford pinto"], ["150", "volkswagen", "volkswagen dasher"], ["30", "bmw", "bmw 2002"], ["244", "ford", "ford mustang ii 2+2"], ["245", "chevrolet", "chevrolet chevette"], ["301", "volkswagen", "vw rabbit custom"], ["193", "dodge", "dodge colt"], ["231", "dodge", "dodge monaco brougham"], ["84", "volvo", "volvo 145e (sw)"], ["310", "plymouth", "plymouth horizon tc3"], ["128", "volvo", "volvo 144ea"], ["69", "ford", "ford pinto runabout"], ["33", "chevrolet", "chevy c20"], ["191", "opel", "opel 1900"], ["250", "bmw", "bmw 320i"], ["376", "chevrolet", "chevrolet cavalier"], ["73", "ford", "ford galaxie 500"], ["312", "fiat", "fiat strada custom"], ["5", "ford", "ford torino"], ["350", "plymouth", "plymouth reliant"], ["369", "volvo", "volvo diesel"], ["272", "ford", "ford futura"], ["185", "audi", "audi 100ls"], ["165", "chevrolet", "chevrolet bel air"], ["46", "chevrolet", "chevrolet impala"], ["125", "fiat", "fiat 128"], ["140", "chevrolet", "chevrolet vega"], ["359", "ford", "ford escort 4w"], ["353", "honda", "honda civic 1300"], ["174", "ford", "ford mustang ii"], ["305", "mercedes", "mercedes benz 300d"], ["393", "honda", "honda civic (auto)"], ["155", "fiat", "fiat 128"], ["179", "toyota", "toyota corona"], ["54", "chevrolet", "chevrolet vega (sw)"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["380", "dodge", "dodge aries se"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["123", "chevrolet", "chevrolet monte carlo s"], ["151", "opel", "opel manta"], ["360", "ford", "ford escort 2h"], ["246", "dodge", "dodge colt m/m"], ["337", "honda", "honda civic 1500 gl"], ["91", "dodge", "dodge colt (sw)"], ["161", "chevrolet", "chevrolet nova"], ["99", "chevrolet", "chevrolet caprice classic"], ["58", "opel", "opel 1900"], ["356", "toyota", "toyota tercel"], ["70", "chevrolet", "chevrolet impala"], ["88", "ford", "ford pinto (sw)"], ["390", "honda", "honda accord"], ["32", "ford", "ford f250"], ["101", "plymouth", "plymouth fury gran sedan"], ["122", "fiat", "fiat 124 sport coupe"], ["236", "ford", "ford granada"], ["39", "ford", "ford pinto"], ["325", "audi", "audi 4000"], ["60", "fiat", "fiat 124b"], ["89", "datsun", "datsun 510 (sw)"], ["221", "chevrolet", "chevy c10"], ["152", "toyota", "toyota corona"], ["136", "chevrolet", "chevrolet nova"], ["294", "ford", "ford ltd landau"], ["276", "datsun", "datsun 510"], ["331", "dodge", "dodge colt"], ["248", "volkswagen", "volkswagen dasher"], ["37", "chevrolet", "chevrolet vega 2300"], ["145", "buick", "buick century luxus (sw)"], ["233", "chevrolet", "chevrolet concours"], ["262", "ford", "ford fairmont (auto)"], ["332", "datsun", "datsun 210"], ["159", "fiat", "fiat x1.9"], ["200", "chevrolet", "chevrolet nova"], ["7", "chevrolet", "chevrolet impala"], ["117", "chevrolet", "chevrolet vega"], ["209", "pontiac", "pontiac ventura sj"], ["190", "fiat", "fiat 131"], ["121", "mercury", "mercury capri v6"], ["277", "dodge", "dodge omni"], ["47", "pontiac", "pontiac catalina brougham"], ["328", "datsun", "datsun 510 hatchback"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the maximum horsepower and the make of the car models with 3 cylinders?	[["97", "mazda rx2 coupe"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["285", "peugeot", "peugeot 604sl"], ["340", " volkswagen", "volkswagen rabbit"], ["273", "dodge", "dodge magnum xe"], ["59", "peugeot", "peugeot 304"], ["216", "plymouth", "plymouth volare premier v8"], ["87", "renault", "renault 12 (sw)"], ["72", "plymouth", "plymouth fury iii"], ["147", "ford", "ford gran torino (sw)"], ["194", "renault", "renault 12tl"], ["355", "datsun", "datsun 210 mpg"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["126", "opel", "opel manta"], ["283", "volvo", "volvo 264gl"], ["82", "ford", "ford gran torino (sw)"], ["8", "plymouth", "plymouth fury iii"], ["338", "renault", "renault lecar deluxe"], ["193", "dodge", "dodge colt"], ["334", "volkswagen", "vw dasher (diesel)"], ["49", "plymouth", "plymouth fury iii"], ["67", "volkswagen", "volkswagen type 3"], ["241", "volkswagen", "volkswagen rabbit custom"], ["191", "opel", "opel 1900"], ["344", "ford", "ford mustang cobra"], ["217", "peugeot", "peugeot 504"], ["246", "dodge", "dodge colt m/m"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["27", "peugeot", "peugeot 504"], ["144", "ford", "ford gran torino"], ["84", "volvo", "volvo 145e (sw)"], ["362", "renault", "renault 18i"], ["91", "dodge", "dodge colt (sw)"], ["187", "volvo", "volvo 244dl"], ["18", "ford", "ford mustang boss 302"], ["226", "renault", "renault 5 gtl"], ["317", "volkswagen", "vw rabbit"], ["384", "volkswagen", "volkswagen rabbit l"], ["186", "peugeot", "peugeot 504"], ["369", "volvo", "volvo diesel"], ["205", "volkswagen", "vw rabbit"], ["350", "plymouth", "plymouth reliant"], ["183", "volkswagen", "volkswagen rabbit"], ["58", "opel", "opel 1900"], ["211", "volkswagen", "volkswagen rabbit"], ["380", "dodge", "dodge aries se"], ["288", "pontiac", "pontiac lemans v6"], ["128", "volvo", "volvo 144ea"], ["151", "opel", "opel manta"], ["96", "ford", "ford gran torino"], ["198", "ford", "ford gran torino"], ["244", "ford", "ford mustang ii 2+2"], ["229", "chevrolet", "chevrolet caprice classic"], ["335", "audi", "audi 5000s (diesel)"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["121", "mercury", "mercury capri v6"], ["155", "fiat", "fiat 128"], ["363", "honda", "honda prelude"], ["301", "volkswagen", "vw rabbit custom"], ["307", "peugeot", "peugeot 504"], ["331", "dodge", "dodge colt"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["371", "datsun", "datsun 810 maxima"], ["44", "ford", "ford torino 500"], ["239", "chrysler", "chrysler cordoba"], ["125", "fiat", "fiat 128"], ["215", "volvo", "volvo 245"], ["250", "bmw", "bmw 320i"], ["86", "peugeot", "peugeot 504 (sw)"], ["310", "plymouth", "plymouth horizon tc3"], ["223", "dodge", "dodge d100"], ["110", "volkswagen", "volkswagen super beetle"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["185", "audi", "audi 100ls"], ["69", "ford", "ford pinto runabout"], ["73", "ford", "ford galaxie 500"], ["78", "chrysler", "chrysler newport royal"], ["174", "ford", "ford mustang ii"], ["89", "datsun", "datsun 510 (sw)"], ["160", "plymouth", "plymouth valiant custom"], ["359", "ford", "ford escort 4w"], ["140", "chevrolet", "chevrolet vega"], ["173", "chevrolet", "chevrolet monza 2+2"], ["176", "ford", "ford pinto"], ["180", "volkswagen", "volkswagen dasher"], ["154", "dodge", "dodge colt"], ["159", "fiat", "fiat x1.9"], ["220", "cadillac", "cadillac seville"], ["240", "ford", "ford thunderbird"], ["294", "ford", "ford ltd landau"], ["293", "chevrolet", "chevrolet caprice classic"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["376", "chevrolet", "chevrolet cavalier"], ["276", "datsun", "datsun 510"], ["165", "chevrolet", "chevrolet bel air"], ["13", "ford", "ford torino (sw)"], ["153", "datsun", "datsun 710"], ["63", "volkswagen", "volkswagen model 111"], ["60", "fiat", "fiat 124b"], ["343", "triumph", "triumph tr7 coupe"], ["11", "citroen", "citroen ds-21 pallas"], ["236", "ford", "ford granada"], ["50", "dodge", "dodge monaco (sw)"], ["62", "datsun", "datsun 1200"], ["150", "volkswagen", "volkswagen dasher"], ["231", "dodge", "dodge monaco brougham"], ["156", "fiat", "fiat 124 tc"], ["332", "datsun", "datsun 210"], ["245", "chevrolet", "chevrolet chevette"], ["221", "chevrolet", "chevy c10"], ["34", "dodge", "dodge d200"], ["32", "ford", "ford f250"], ["30", "bmw", "bmw 2002"], ["272", "ford", "ford futura"], ["360", "ford", "ford escort 2h"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["54", "chevrolet", "chevrolet vega (sw)"], ["166", "plymouth", "plymouth grand fury"], ["305", "mercedes", "mercedes benz 300d"], ["5", "ford", "ford torino"], ["120", "ford", "ford pinto"], ["129", "dodge", "dodge dart custom"], ["182", "ford", "ford pinto"], ["212", "datsun", "datsun b-210"], ["40", "volkswagen", "volkswagen super beetle 117"], ["356", "toyota", "toyota tercel"], ["66", "dodge", "dodge colt hardtop"], ["101", "plymouth", "plymouth fury gran sedan"], ["48", "ford", "ford galaxie 500"], ["328", "datsun", "datsun 510 hatchback"], ["190", "fiat", "fiat 131"], ["146", "dodge", "dodge coronet custom (sw)"], ["319", "chevrolet", "chevrolet chevette"], ["298", "ford", "ford country squire (sw)"], ["277", "dodge", "dodge omni"], ["262", "ford", "ford fairmont (auto)"], ["286", "volkswagen", "volkswagen scirocco"], ["374", "ford", "ford granada gl"], ["33", "chevrolet", "chevy c20"], ["311", "datsun", "datsun 210"], ["97", "dodge", "dodge coronet custom"], ["123", "chevrolet", "chevrolet monte carlo s"], ["248", "volkswagen", "volkswagen dasher"], ["382", "ford", "ford fairmont futura"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the largest amount of horsepower for the models with 3 cylinders and what make is it?	[["97", "mazda rx2 coupe"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["216", "plymouth", "plymouth volare premier v8"], ["355", "datsun", "datsun 210 mpg"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["334", "volkswagen", "vw dasher (diesel)"], ["211", "volkswagen", "volkswagen rabbit"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["294", "ford", "ford ltd landau"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["262", "ford", "ford fairmont (auto)"], ["87", "renault", "renault 12 (sw)"], ["69", "ford", "ford pinto runabout"], ["183", "volkswagen", "volkswagen rabbit"], ["340", " volkswagen", "volkswagen rabbit"], ["293", "chevrolet", "chevrolet caprice classic"], ["371", "datsun", "datsun 810 maxima"], ["99", "chevrolet", "chevrolet caprice classic"], ["150", "volkswagen", "volkswagen dasher"], ["180", "volkswagen", "volkswagen dasher"], ["205", "volkswagen", "vw rabbit"], ["165", "chevrolet", "chevrolet bel air"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["110", "volkswagen", "volkswagen super beetle"], ["141", "chevrolet", "chevrolet chevelle malibu classic"], ["173", "chevrolet", "chevrolet monza 2+2"], ["350", "plymouth", "plymouth reliant"], ["362", "renault", "renault 18i"], ["317", "volkswagen", "vw rabbit"], ["272", "ford", "ford futura"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["50", "dodge", "dodge monaco (sw)"], ["229", "chevrolet", "chevrolet caprice classic"], ["382", "ford", "ford fairmont futura"], ["301", "volkswagen", "vw rabbit custom"], ["241", "volkswagen", "volkswagen rabbit custom"], ["100", "ford", "ford ltd"], ["226", "renault", "renault 5 gtl"], ["54", "chevrolet", "chevrolet vega (sw)"], ["194", "renault", "renault 12tl"], ["182", "ford", "ford pinto"], ["176", "ford", "ford pinto"], ["169", "chevrolet", "chevrolet chevelle malibu"], ["160", "plymouth", "plymouth valiant custom"], ["167", "ford", "ford ltd"], ["73", "ford", "ford galaxie 500"], ["248", "volkswagen", "volkswagen dasher"], ["140", "chevrolet", "chevrolet vega"], ["263", "ford", "ford fairmont (man)"], ["48", "ford", "ford galaxie 500"], ["101", "plymouth", "plymouth fury gran sedan"], ["208", "ford", "ford granada ghia"], ["209", "pontiac", "pontiac ventura sj"], ["235", "plymouth", "plymouth volare custom"], ["322", "ford", "ford fairmont"], ["290", "ford", "ford fairmont 4"], ["380", "dodge", "dodge aries se"], ["82", "ford", "ford gran torino (sw)"], ["310", "plymouth", "plymouth horizon tc3"], ["191", "opel", "opel 1900"], ["120", "ford", "ford pinto"], ["88", "ford", "ford pinto (sw)"], ["239", "chrysler", "chrysler cordoba"], ["233", "chevrolet", "chevrolet concours"], ["67", "volkswagen", "volkswagen type 3"], ["89", "datsun", "datsun 510 (sw)"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["58", "opel", "opel 1900"], ["384", "volkswagen", "volkswagen rabbit l"], ["161", "chevrolet", "chevrolet nova"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["198", "ford", "ford gran torino"], ["231", "dodge", "dodge monaco brougham"], ["147", "ford", "ford gran torino (sw)"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["221", "chevrolet", "chevy c10"], ["117", "chevrolet", "chevrolet vega"], ["286", "volkswagen", "volkswagen scirocco"], ["314", "chevrolet", "chevrolet citation"], ["299", "chevrolet", "chevrolet malibu classic (sw)"], ["311", "datsun", "datsun 210"], ["207", "dodge", "dodge aspen se"], ["359", "ford", "ford escort 4w"], ["245", "chevrolet", "chevrolet chevette"], ["288", "pontiac", "pontiac lemans v6"], ["63", "volkswagen", "volkswagen model 111"], ["285", "peugeot", "peugeot 604sl"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["181", "datsun", "datsun 710"], ["214", "ford", "ford pinto"], ["37", "chevrolet", "chevrolet vega 2300"], ["137", "datsun", "datsun b210"], ["40", "volkswagen", "volkswagen super beetle 117"], ["266", "buick", "buick century special"], ["236", "ford", "ford granada"], ["276", "datsun", "datsun 510"], ["16", "dodge", "dodge challenger se"], ["217", "peugeot", "peugeot 504"], ["332", "datsun", "datsun 210"], ["204", "chevrolet", "chevrolet woody"], ["338", "renault", "renault lecar deluxe"], ["95", "chevrolet", "chevrolet malibu"], ["168", "buick", "buick century"], ["151", "opel", "opel manta"], ["179", "toyota", "toyota corona"], ["68", "chevrolet", "chevrolet vega"], ["144", "ford", "ford gran torino"], ["319", "chevrolet", "chevrolet chevette"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["403", "volkswagen", "vw pickup"], ["121", "mercury", "mercury capri v6"], ["306", "cadillac", "cadillac eldorado"], ["370", "toyota", "toyota cressida"], ["131", "toyota", "toyota mark ii"], ["153", "datsun", "datsun 710"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["164", "pontiac", "pontiac catalina"], ["62", "datsun", "datsun 1200"], ["21", "toyota", "toyota corona mark ii"], ["13", "ford", "ford torino (sw)"], ["224", "honda", "honda accord cvcc"], ["218", "toyota", "toyota mark ii"], ["363", "honda", "honda prelude"], ["78", "chrysler", "chrysler newport royal"], ["43", "chevrolet", "chevrolet chevelle malibu"], ["47", "pontiac", "pontiac catalina brougham"], ["349", "chevrolet", "chevrolet citation"], ["118", "datsun", "datsun 610"], ["258", "dodge", "dodge diplomat"], ["90", "toyota", "toyota corona mark ii (sw)"], ["321", "chevrolet", "chevrolet citation"], ["185", "audi", "audi 100ls"], ["152", "toyota", "toyota corona"], ["5", "ford", "ford torino"], ["39", "ford", "ford pinto"], ["401", "chevrolet", "chevrolet camaro"], ["96", "ford", "ford gran torino"], ["356", "toyota", "toyota tercel"], ["212", "datsun", "datsun b-210"], ["374", "ford", "ford granada gl"], ["178", "pontiac", "pontiac astro"], ["145", "buick", "buick century luxus (sw)"], ["33", "chevrolet", "chevy c20"], ["375", "chrysler", "chrysler lebaron salon"], ["138", "ford", "ford pinto"], ["395", "buick", "buick century limited"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Which model saves the most gasoline? That is to say, have the maximum miles per gallon.	[["citroen"]]	2,048	Answer:
Table car_names: [["MakeId", "Model", "Make"], ["355", "datsun", "datsun 210 mpg"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["334", "volkswagen", "vw dasher (diesel)"], ["110", "volkswagen", "volkswagen super beetle"], ["173", "chevrolet", "chevrolet monza 2+2"], ["335", "audi", "audi 5000s (diesel)"], ["180", "volkswagen", "volkswagen dasher"], ["205", "volkswagen", "vw rabbit"], ["179", "toyota", "toyota corona"], ["90", "toyota", "toyota corona mark ii (sw)"], ["245", "chevrolet", "chevrolet chevette"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["211", "volkswagen", "volkswagen rabbit"], ["275", "toyota", "toyota corona"], ["165", "chevrolet", "chevrolet bel air"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["43", "chevrolet", "chevrolet chevelle malibu"], ["216", "plymouth", "plymouth volare premier v8"], ["150", "volkswagen", "volkswagen dasher"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["317", "volkswagen", "vw rabbit"], ["21", "toyota", "toyota corona mark ii"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["340", " volkswagen", "volkswagen rabbit"], ["40", "volkswagen", "volkswagen super beetle 117"], ["286", "volkswagen", "volkswagen scirocco"], ["67", "volkswagen", "volkswagen type 3"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["82", "ford", "ford gran torino (sw)"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["371", "datsun", "datsun 810 maxima"], ["217", "peugeot", "peugeot 504"], ["99", "chevrolet", "chevrolet caprice classic"], ["59", "peugeot", "peugeot 304"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["215", "volvo", "volvo 245"], ["185", "audi", "audi 100ls"], ["229", "chevrolet", "chevrolet caprice classic"], ["54", "chevrolet", "chevrolet vega (sw)"], ["370", "toyota", "toyota cressida"], ["285", "peugeot", "peugeot 604sl"], ["293", "chevrolet", "chevrolet caprice classic"], ["301", "volkswagen", "vw rabbit custom"], ["70", "chevrolet", "chevrolet impala"], ["319", "chevrolet", "chevrolet chevette"], ["116", "toyota", "toyota carina"], ["356", "toyota", "toyota tercel"], ["63", "volkswagen", "volkswagen model 111"], ["152", "toyota", "toyota corona"], ["87", "renault", "renault 12 (sw)"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["46", "chevrolet", "chevrolet impala"], ["278", "toyota", "toyota celica gt liftback"], ["140", "chevrolet", "chevrolet vega"], ["183", "volkswagen", "volkswagen rabbit"], ["11", "citroen", "citroen ds-21 pallas"], ["200", "chevrolet", "chevrolet nova"], ["73", "ford", "ford galaxie 500"], ["305", "mercedes", "mercedes benz 300d"], ["19", "chevrolet", "chevrolet monte carlo"], ["123", "chevrolet", "chevrolet monte carlo s"], ["141", "chevrolet", "chevrolet chevelle malibu classic"], ["299", "chevrolet", "chevrolet malibu classic (sw)"], ["325", "audi", "audi 4000"], ["326", "toyota", "toyota corona liftback"], ["44", "ford", "ford torino 500"], ["174", "ford", "ford mustang ii"], ["38", "toyota", "toyota corona"], ["283", "volvo", "volvo 264gl"], ["399", "toyota", "toyota celica gt"], ["209", "pontiac", "pontiac ventura sj"], ["48", "ford", "ford galaxie 500"], ["186", "peugeot", "peugeot 504"], ["384", "volkswagen", "volkswagen rabbit l"], ["338", "renault", "renault lecar deluxe"], ["328", "datsun", "datsun 510 hatchback"], ["241", "volkswagen", "volkswagen rabbit custom"], ["307", "peugeot", "peugeot 504"], ["147", "ford", "ford gran torino (sw)"], ["361", "volkswagen", "volkswagen jetta"], ["27", "peugeot", "peugeot 504"], ["175", "toyota", "toyota corolla"], ["248", "volkswagen", "volkswagen dasher"], ["131", "toyota", "toyota mark ii"], ["369", "volvo", "volvo diesel"], ["161", "chevrolet", "chevrolet nova"], ["37", "chevrolet", "chevrolet vega 2300"], ["250", "bmw", "bmw 320i"], ["226", "renault", "renault 5 gtl"], ["344", "ford", "ford mustang cobra"], ["389", "nissan", "nissan stanza xe"], ["203", "chevrolet", "chevrolet chevette"], ["89", "datsun", "datsun 510 (sw)"], ["117", "chevrolet", "chevrolet vega"], ["7", "chevrolet", "chevrolet impala"], ["50", "dodge", "dodge monaco (sw)"], ["362", "renault", "renault 18i"], ["169", "chevrolet", "chevrolet chevelle malibu"], ["111", "chevrolet", "chevrolet impala"], ["101", "plymouth", "plymouth fury gran sedan"], ["208", "ford", "ford granada ghia"], ["359", "ford", "ford escort 4w"], ["239", "chrysler", "chrysler cordoba"], ["318", "toyota", "toyota corolla tercel"], ["86", "peugeot", "peugeot 504 (sw)"], ["65", "toyota", "toyota corona hardtop"], ["274", "chevrolet", "chevrolet chevette"], ["376", "chevrolet", "chevrolet cavalier"], ["343", "triumph", "triumph tr7 coupe"], ["144", "ford", "ford gran torino"], ["213", "toyota", "toyota corolla"], ["84", "volvo", "volvo 145e (sw)"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["187", "volvo", "volvo 244dl"], ["47", "pontiac", "pontiac catalina brougham"], ["262", "ford", "ford fairmont (auto)"], ["218", "toyota", "toyota mark ii"], ["288", "pontiac", "pontiac lemans v6"], ["390", "honda", "honda accord"], ["198", "ford", "ford gran torino"], ["136", "chevrolet", "chevrolet nova"], ["106", "chevrolet", "chevrolet nova custom"], ["244", "ford", "ford mustang ii 2+2"], ["95", "chevrolet", "chevrolet malibu"], ["96", "ford", "ford gran torino"], ["243", "toyota", "toyota corolla liftback"], ["240", "ford", "ford thunderbird"], ["332", "datsun", "datsun 210"], ["231", "dodge", "dodge monaco brougham"], ["403", "volkswagen", "vw pickup"], ["68", "chevrolet", "chevrolet vega"], ["153", "datsun", "datsun 710"], ["9", "pontiac", "pontiac catalina"], ["62", "datsun", "datsun 1200"], ["360", "ford", "ford escort 2h"], ["127", "audi", "audi 100ls"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["13", "ford", "ford torino (sw)"], ["212", "datsun", "datsun b-210"], ["290", "ford", "ford fairmont 4"], ["329", "toyota", "toyota corolla"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the car model with the highest mpg ?	[["citroen"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["318", "38.1", "4", "89.0", "60", "1968", "18.8", "1980"], ["317", "41.5", "4", "98.0", "76", "2144", "14.7", "1980"], ["327", "31.3", "4", "120.0", "75", "2542", "17.5", "1980"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["325", "34.3", "4", "97.0", "78", "2188", "15.8", "1980"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"], ["330", "46.6", "4", "86.0", "65", "2110", "17.9", "1980"], ["322", "26.4", "4", "140.0", "88", "2870", "18.1", "1980"], ["209", "18.5", "6", "250.0", "110", "3645", "16.2", "1976"], ["337", "44.6", "4", "91.0", "67", "1850", "13.8", "1980"], ["296", "18.2", "8", "318.0", "135", "3830", "15.2", "1979"], ["332", "40.8", "4", "85.0", "65", "2110", "19.2", "1980"], ["106", "16", "6", "250.0", "100", "3278", "18.0", "1973"], ["340", "29.8", "4", "89.0", "62", "1845", "15.3", "1980"], ["326", "29.8", "4", "134.0", "90", "2711", "15.5", "1980"], ["341", "32.7", "6", "168.0", "132", "2910", "11.4", "1980"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["217", "19", "4", "120.0", "88", "3270", "21.9", "1976"], ["82", "13", "8", "302.0", "140", "4294", "16.0", "1972"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["169", "16", "6", "250.0", "105", "3897", "18.5", "1975"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["297", "16.9", "8", "350.0", "155", "4360", "14.9", "1979"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["335", "36.4", "5", "121.0", "67", "2950", "19.9", "1980"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["171", "18", "6", "225.0", "95", "3785", "19.0", "1975"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["385", "37", "4", "91.0", "68", "2025", "18.2", "1982"], ["220", "16.5", "8", "350.0", "180", "4380", "12.1", "1976"], ["84", "18", "4", "121.0", "112", "2933", "14.5", "1972"], ["395", "25", "6", "181.0", "110", "2945", "16.4", "1982"], ["334", "43.4", "4", "90.0", "48", "2335", "23.7", "1980"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["382", "24", "4", "140.0", "92", "2865", "16.4", "1982"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["285", "16.2", "6", "163.0", "133", "3410", "15.8", "1978"], ["357", "34.1", "4", "91.0", "68", "1985", "16.0", "1981"], ["320", "37.2", "4", "86.0", "65", "2019", "16.4", "1980"], ["302", "34.1", "4", "86.0", "65", "1975", "15.2", "1979"], ["354", "32.3", "4", "97.0", "67", "2065", "17.8", "1981"], ["81", "13", "8", "307.0", "130", "4098", "14.0", "1972"], ["108", "18", "6", "250.0", "88", "3021", "16.5", "1973"], ["400", "36", "4", "135.0", "84", "2370", "13.0", "1982"], ["303", "35.7", "4", "98.0", "80", "1915", "14.4", "1979"], ["293", "17", "8", "305.0", "130", "3840", "15.4", "1979"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["88", "22", "4", "122.0", "86", "2395", "16.0", "1972"], ["319", "32.1", "4", "98.0", "70", "2120", "15.5", "1980"], ["124", "16", "8", "400.0", "230", "4278", "9.5", "1973"], ["195", "17.5", "8", "305.0", "140", "4215", "13.0", "1976"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["402", "27", "4", "140.0", "86", "2790", "15.6", "1982"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["379", "31", "4", "112.0", "85", "2575", "16.2", "1982"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["375", "17.6", "6", "225.0", "85", "3465", "16.6", "1981"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["42", "16", "6", "225.0", "105", "3439", "15.5", "1971"], ["80", "15", "8", "304.0", "150", "3892", "12.5", "1972"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the average horsepower of the cars before 1980?	[["111.13291139240506"]]	2,048	Answer:
Table cars_data: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["318", "38.1", "4", "89.0", "60", "1968", "18.8", "1980"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["317", "41.5", "4", "98.0", "76", "2144", "14.7", "1980"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["327", "31.3", "4", "120.0", "75", "2542", "17.5", "1980"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["325", "34.3", "4", "97.0", "78", "2188", "15.8", "1980"], ["340", "29.8", "4", "89.0", "62", "1845", "15.3", "1980"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"], ["337", "44.6", "4", "91.0", "67", "1850", "13.8", "1980"], ["106", "16", "6", "250.0", "100", "3278", "18.0", "1973"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["209", "18.5", "6", "250.0", "110", "3645", "16.2", "1976"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["169", "16", "6", "250.0", "105", "3897", "18.5", "1975"], ["322", "26.4", "4", "140.0", "88", "2870", "18.1", "1980"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["326", "29.8", "4", "134.0", "90", "2711", "15.5", "1980"], ["82", "13", "8", "302.0", "140", "4294", "16.0", "1972"], ["330", "46.6", "4", "86.0", "65", "2110", "17.9", "1980"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["171", "18", "6", "225.0", "95", "3785", "19.0", "1975"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["297", "16.9", "8", "350.0", "155", "4360", "14.9", "1979"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["296", "18.2", "8", "318.0", "135", "3830", "15.2", "1979"], ["217", "19", "4", "120.0", "88", "3270", "21.9", "1976"], ["332", "40.8", "4", "85.0", "65", "2110", "19.2", "1980"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["341", "32.7", "6", "168.0", "132", "2910", "11.4", "1980"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["124", "16", "8", "400.0", "230", "4278", "9.5", "1973"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["285", "16.2", "6", "163.0", "133", "3410", "15.8", "1978"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"], ["80", "15", "8", "304.0", "150", "3892", "12.5", "1972"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["240", "16", "8", "351.0", "149", "4335", "14.5", "1977"], ["350", "30", "4", "135.0", "84", "2385", "12.9", "1981"], ["81", "13", "8", "307.0", "130", "4098", "14.0", "1972"], ["164", "16", "8", "400.0", "170", "4668", "11.5", "1975"], ["16", "15", "8", "383.0", "170", "3563", "10.0", "1970"], ["220", "16.5", "8", "350.0", "180", "4380", "12.1", "1976"], ["382", "24", "4", "140.0", "92", "2865", "16.4", "1982"], ["88", "22", "4", "122.0", "86", "2395", "16.0", "1972"], ["293", "17", "8", "305.0", "130", "3840", "15.4", "1979"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["108", "18", "6", "250.0", "88", "3021", "16.5", "1973"], ["400", "36", "4", "135.0", "84", "2370", "13.0", "1982"], ["195", "17.5", "8", "305.0", "140", "4215", "13.0", "1976"], ["335", "36.4", "5", "121.0", "67", "2950", "19.9", "1980"], ["402", "27", "4", "140.0", "86", "2790", "15.6", "1982"], ["42", "16", "6", "225.0", "105", "3439", "15.5", "1971"], ["78", "13", "8", "400.0", "190", "4422", "12.5", "1972"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["385", "37", "4", "91.0", "68", "2025", "18.2", "1982"], ["84", "18", "4", "121.0", "112", "2933", "14.5", "1972"], ["303", "35.7", "4", "98.0", "80", "1915", "14.4", "1979"], ["320", "37.2", "4", "86.0", "65", "2019", "16.4", "1980"], ["77", "12", "8", "350.0", "160", "4456", "13.5", "1972"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the average horsepower for all cars produced before 1980 ?	[["111.13291139240506"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["84", "volvo", "volvo 145e (sw)"], ["128", "volvo", "volvo 144ea"], ["215", "volvo", "volvo 245"], ["283", "volvo", "volvo 264gl"], ["187", "volvo", "volvo 244dl"], ["369", "volvo", "volvo diesel"], ["340", " volkswagen", "volkswagen rabbit"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["335", "audi", "audi 5000s (diesel)"], ["334", "volkswagen", "vw dasher (diesel)"], ["110", "volkswagen", "volkswagen super beetle"], ["180", "volkswagen", "volkswagen dasher"], ["286", "volkswagen", "volkswagen scirocco"], ["338", "renault", "renault lecar deluxe"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["67", "volkswagen", "volkswagen type 3"], ["241", "volkswagen", "volkswagen rabbit custom"], ["384", "volkswagen", "volkswagen rabbit l"], ["185", "audi", "audi 100ls"], ["150", "volkswagen", "volkswagen dasher"], ["307", "peugeot", "peugeot 504"], ["40", "volkswagen", "volkswagen super beetle 117"], ["285", "peugeot", "peugeot 604sl"], ["205", "volkswagen", "vw rabbit"], ["325", "audi", "audi 4000"], ["317", "volkswagen", "vw rabbit"], ["59", "peugeot", "peugeot 304"], ["248", "volkswagen", "volkswagen dasher"], ["63", "volkswagen", "volkswagen model 111"], ["361", "volkswagen", "volkswagen jetta"], ["87", "renault", "renault 12 (sw)"], ["183", "volkswagen", "volkswagen rabbit"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["211", "volkswagen", "volkswagen rabbit"], ["301", "volkswagen", "vw rabbit custom"], ["149", "audi", "audi fox"], ["362", "renault", "renault 18i"], ["50", "dodge", "dodge monaco (sw)"], ["127", "audi", "audi 100ls"], ["186", "peugeot", "peugeot 504"], ["376", "chevrolet", "chevrolet cavalier"], ["305", "mercedes", "mercedes benz 300d"], ["27", "peugeot", "peugeot 504"], ["229", "chevrolet", "chevrolet caprice classic"], ["403", "volkswagen", "vw pickup"], ["86", "peugeot", "peugeot 504 (sw)"], ["282", "audi", "audi 5000"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["356", "toyota", "toyota tercel"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["217", "peugeot", "peugeot 504"], ["116", "toyota", "toyota carina"], ["126", "opel", "opel manta"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["122", "fiat", "fiat 124 sport coupe"], ["355", "datsun", "datsun 210 mpg"], ["370", "toyota", "toyota cressida"], ["293", "chevrolet", "chevrolet caprice classic"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["231", "dodge", "dodge monaco brougham"], ["36", "datsun", "datsun pl510"], ["250", "bmw", "bmw 320i"], ["173", "chevrolet", "chevrolet monza 2+2"], ["62", "datsun", "datsun 1200"], ["371", "datsun", "datsun 810 maxima"], ["89", "datsun", "datsun 510 (sw)"], ["225", "buick", "buick opel isuzu deluxe"], ["249", "datsun", "datsun 810"], ["145", "buick", "buick century luxus (sw)"], ["158", "subaru", "subaru"], ["148", "amc", "amc matador (sw)"], ["247", "subaru", "subaru dl"], ["152", "toyota", "toyota corona"], ["277", "dodge", "dodge omni"], ["179", "toyota", "toyota corona"], ["38", "toyota", "toyota corona"], ["375", "chrysler", "chrysler lebaron salon"], ["102", "chrysler", "chrysler new yorker brougham"], ["297", "buick", "buick estate wagon (sw)"], ["366", "mazda", "mazda 626"], ["131", "toyota", "toyota mark ii"], ["306", "cadillac", "cadillac eldorado"], ["82", "ford", "ford gran torino (sw)"], ["275", "toyota", "toyota corona"], ["239", "chrysler", "chrysler cordoba"], ["46", "chevrolet", "chevrolet impala"], ["194", "renault", "renault 12tl"], ["106", "chevrolet", "chevrolet nova custom"], ["25", "datsun", "datsun pl510"], ["312", "fiat", "fiat strada custom"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["245", "chevrolet", "chevrolet chevette"], ["30", "bmw", "bmw 2002"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["28", "audi", "audi 100 ls"], ["320", "datsun", "datsun 310"], ["19", "chevrolet", "chevrolet monte carlo"], ["218", "toyota", "toyota mark ii"], ["343", "triumph", "triumph tr7 coupe"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["220", "cadillac", "cadillac seville"], ["348", "dodge", "dodge aries wagon (sw)"], ["318", "toyota", "toyota corolla tercel"], ["328", "datsun", "datsun 510 hatchback"], ["34", "dodge", "dodge d200"], ["80", "amc", "amc matador (sw)"], ["200", "chevrolet", "chevrolet nova"], ["90", "toyota", "toyota corona mark ii (sw)"], ["136", "chevrolet", "chevrolet nova"], ["271", "buick", "buick regal sport coupe (turbo)"], ["161", "chevrolet", "chevrolet nova"], ["99", "chevrolet", "chevrolet caprice classic"], ["319", "chevrolet", "chevrolet chevette"], ["273", "dodge", "dodge magnum xe"], ["123", "chevrolet", "chevrolet monte carlo s"], ["354", "subaru", "subaru"], ["20", "buick", "buick estate wagon (sw)"], ["7", "chevrolet", "chevrolet impala"], ["276", "datsun", "datsun 510"], ["170", "amc", "amc matador"], ["118", "datsun", "datsun 610"], ["216", "plymouth", "plymouth volare premier v8"], ["111", "chevrolet", "chevrolet impala"], ["365", "datsun", "datsun 200sx"], ["125", "fiat", "fiat 128"], ["262", "ford", "ford fairmont (auto)"], ["397", "chrysler", "chrysler lebaron medallion"], ["54", "chevrolet", "chevrolet vega (sw)"], ["339", "subaru", "subaru dl"], ["281", "datsun", "datsun 200-sx"], ["45", "amc", "amc matador"], ["13", "ford", "ford torino (sw)"], ["70", "chevrolet", "chevrolet impala"], ["193", "dodge", "dodge colt"], ["146", "dodge", "dodge coronet custom (sw)"], ["21", "toyota", "toyota corona mark ii"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["337", "honda", "honda civic 1500 gl"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the average edispl of the cars of model volvo?	[["133.5"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["84", "volvo", "volvo 145e (sw)"], ["128", "volvo", "volvo 144ea"], ["215", "volvo", "volvo 245"], ["283", "volvo", "volvo 264gl"], ["187", "volvo", "volvo 244dl"], ["369", "volvo", "volvo diesel"], ["335", "audi", "audi 5000s (diesel)"], ["334", "volkswagen", "vw dasher (diesel)"], ["340", " volkswagen", "volkswagen rabbit"], ["185", "audi", "audi 100ls"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["362", "renault", "renault 18i"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["338", "renault", "renault lecar deluxe"], ["325", "audi", "audi 4000"], ["127", "audi", "audi 100ls"], ["150", "volkswagen", "volkswagen dasher"], ["87", "renault", "renault 12 (sw)"], ["285", "peugeot", "peugeot 604sl"], ["286", "volkswagen", "volkswagen scirocco"], ["180", "volkswagen", "volkswagen dasher"], ["248", "volkswagen", "volkswagen dasher"], ["355", "datsun", "datsun 210 mpg"], ["384", "volkswagen", "volkswagen rabbit l"], ["241", "volkswagen", "volkswagen rabbit custom"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["307", "peugeot", "peugeot 504"], ["110", "volkswagen", "volkswagen super beetle"], ["317", "volkswagen", "vw rabbit"], ["247", "subaru", "subaru dl"], ["205", "volkswagen", "vw rabbit"], ["158", "subaru", "subaru"], ["320", "datsun", "datsun 310"], ["149", "audi", "audi fox"], ["282", "audi", "audi 5000"], ["301", "volkswagen", "vw rabbit custom"], ["229", "chevrolet", "chevrolet caprice classic"], ["25", "datsun", "datsun pl510"], ["249", "datsun", "datsun 810"], ["67", "volkswagen", "volkswagen type 3"], ["36", "datsun", "datsun pl510"], ["59", "peugeot", "peugeot 304"], ["376", "chevrolet", "chevrolet cavalier"], ["239", "chrysler", "chrysler cordoba"], ["89", "datsun", "datsun 510 (sw)"], ["339", "subaru", "subaru dl"], ["211", "volkswagen", "volkswagen rabbit"], ["356", "toyota", "toyota tercel"], ["183", "volkswagen", "volkswagen rabbit"], ["250", "bmw", "bmw 320i"], ["403", "volkswagen", "vw pickup"], ["194", "renault", "renault 12tl"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["371", "datsun", "datsun 810 maxima"], ["131", "toyota", "toyota mark ii"], ["62", "datsun", "datsun 1200"], ["370", "toyota", "toyota cressida"], ["216", "plymouth", "plymouth volare premier v8"], ["354", "subaru", "subaru"], ["375", "chrysler", "chrysler lebaron salon"], ["361", "volkswagen", "volkswagen jetta"], ["126", "opel", "opel manta"], ["186", "peugeot", "peugeot 504"], ["152", "toyota", "toyota corona"], ["116", "toyota", "toyota carina"], ["276", "datsun", "datsun 510"], ["86", "peugeot", "peugeot 504 (sw)"], ["102", "chrysler", "chrysler new yorker brougham"], ["275", "toyota", "toyota corona"], ["218", "toyota", "toyota mark ii"], ["277", "dodge", "dodge omni"], ["27", "peugeot", "peugeot 504"], ["394", "datsun", "datsun 310 gx"], ["50", "dodge", "dodge monaco (sw)"], ["328", "datsun", "datsun 510 hatchback"], ["305", "mercedes", "mercedes benz 300d"], ["217", "peugeot", "peugeot 504"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["366", "mazda", "mazda 626"], ["293", "chevrolet", "chevrolet caprice classic"], ["397", "chrysler", "chrysler lebaron medallion"], ["118", "datsun", "datsun 610"], ["40", "volkswagen", "volkswagen super beetle 117"], ["38", "toyota", "toyota corona"], ["365", "datsun", "datsun 200sx"], ["179", "toyota", "toyota corona"], ["262", "ford", "ford fairmont (auto)"], ["322", "ford", "ford fairmont"], ["125", "fiat", "fiat 128"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["30", "bmw", "bmw 2002"], ["281", "datsun", "datsun 200-sx"], ["225", "buick", "buick opel isuzu deluxe"], ["28", "audi", "audi 100 ls"], ["311", "datsun", "datsun 210"], ["341", "datsun", "datsun 280-zx"], ["314", "chevrolet", "chevrolet citation"], ["306", "cadillac", "cadillac eldorado"], ["386", "mazda", "mazda glc custom"], ["34", "dodge", "dodge d200"], ["258", "dodge", "dodge diplomat"], ["90", "toyota", "toyota corona mark ii (sw)"], ["181", "datsun", "datsun 710"], ["122", "fiat", "fiat 124 sport coupe"], ["13", "ford", "ford torino (sw)"], ["220", "cadillac", "cadillac seville"], ["46", "chevrolet", "chevrolet impala"], ["231", "dodge", "dodge monaco brougham"], ["226", "renault", "renault 5 gtl"], ["380", "dodge", "dodge aries se"], ["389", "nissan", "nissan stanza xe"], ["245", "chevrolet", "chevrolet chevette"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["121", "mercury", "mercury capri v6"], ["78", "chrysler", "chrysler newport royal"], ["148", "amc", "amc matador (sw)"], ["312", "fiat", "fiat strada custom"], ["145", "buick", "buick century luxus (sw)"], ["254", "mazda", "mazda glc deluxe"], ["319", "chevrolet", "chevrolet chevette"], ["321", "chevrolet", "chevrolet citation"], ["82", "ford", "ford gran torino (sw)"], ["65", "toyota", "toyota corona hardtop"], ["153", "datsun", "datsun 710"], ["349", "chevrolet", "chevrolet citation"], ["146", "dodge", "dodge coronet custom (sw)"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["348", "dodge", "dodge aries wagon (sw)"], ["327", "mazda", "mazda 626"], ["155", "fiat", "fiat 128"], ["21", "toyota", "toyota corona mark ii"], ["330", "mazda", "mazda glc"], ["318", "toyota", "toyota corolla tercel"], ["63", "volkswagen", "volkswagen model 111"], ["390", "honda", "honda accord"], ["70", "chevrolet", "chevrolet impala"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["173", "chevrolet", "chevrolet monza 2+2"], ["337", "honda", "honda civic 1500 gl"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the average edispl for all volvos?	[["133.5"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["47", "14", "8", "400.0", "175", "4464", "11.5", "1971"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["136", "15", "6", "250.0", "100", "3336", "17.0", "1974"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["43", "17", "6", "250.0", "100", "3329", "15.5", "1971"], ["185", "23", "4", "115.0", "95", "2694", "15.0", "1975"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["51", "13", "8", "400.0", "170", "4746", "12.0", "1971"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["350", "30", "4", "135.0", "84", "2385", "12.9", "1981"], ["163", "15", "6", "250.0", "72", "3158", "19.5", "1975"], ["223", "13", "8", "318.0", "150", "3755", "14.0", "1976"], ["396", "38", "6", "262.0", "85", "3015", "17.0", "1982"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["379", "31", "4", "112.0", "85", "2575", "16.2", "1982"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["57", "23", "4", "122.0", "86", "2220", "14.0", "1971"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["397", "26", "4", "156.0", "92", "2585", "14.5", "1982"], ["52", "13", "8", "400.0", "175", "5140", "12.0", "1971"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["172", "21", "6", "231.0", "110", "3039", "15.0", "1975"], ["135", "19", "6", "232.0", "100", "2901", "16.0", "1974"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["398", "22", "6", "232.0", "112", "2835", "14.7", "1982"], ["293", "17", "8", "305.0", "130", "3840", "15.4", "1979"], ["146", "14", "8", "318.0", "150", "4457", "13.5", "1974"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["232", "15", "8", "302.0", "130", "4295", "14.9", "1977"], ["201", "24", "6", "200.0", "81", "3012", "17.6", "1976"], ["400", "36", "4", "135.0", "84", "2370", "13.0", "1982"], ["200", "22", "6", "250.0", "105", "3353", "14.5", "1976"], ["385", "37", "4", "91.0", "68", "2025", "18.2", "1982"], ["164", "16", "8", "400.0", "170", "4668", "11.5", "1975"], ["399", "32", "4", "144.0", "96", "2665", "13.9", "1982"], ["179", "24", "4", "134.0", "96", "2702", "13.5", "1975"], ["80", "15", "8", "304.0", "150", "3892", "12.5", "1972"], ["112", "12", "8", "400.0", "167", "4906", "12.5", "1973"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["381", "27", "4", "151.0", "90", "2735", "18.0", "1982"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["283", "17", "6", "163.0", "125", "3140", "13.6", "1978"], ["121", "21", "6", "155.0", "107", "2472", "14.0", "1973"], ["42", "16", "6", "225.0", "105", "3439", "15.5", "1971"], ["108", "18", "6", "250.0", "88", "3021", "16.5", "1973"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["401", "27", "4", "151.0", "90", "2950", "17.3", "1982"], ["49", "14", "8", "318.0", "150", "4096", "13.0", "1971"], ["268", "18.6", "6", "225.0", "110", "3620", "18.7", "1978"], ["166", "16", "8", "318.0", "150", "4498", "14.5", "1975"], ["169", "16", "6", "250.0", "105", "3897", "18.5", "1975"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the maximum accelerate for different number of cylinders?	[["13.5", "3"], ["24.8", "4"], ["20.1", "5"], ["21.0", "6"], ["22.2", "8"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["350", "30", "4", "135.0", "84", "2385", "12.9", "1981"], ["185", "23", "4", "115.0", "95", "2694", "15.0", "1975"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["47", "14", "8", "400.0", "175", "4464", "11.5", "1971"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["136", "15", "6", "250.0", "100", "3336", "17.0", "1974"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["51", "13", "8", "400.0", "170", "4746", "12.0", "1971"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["43", "17", "6", "250.0", "100", "3329", "15.5", "1971"], ["163", "15", "6", "250.0", "72", "3158", "19.5", "1975"], ["379", "31", "4", "112.0", "85", "2575", "16.2", "1982"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["223", "13", "8", "318.0", "150", "3755", "14.0", "1976"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["293", "17", "8", "305.0", "130", "3840", "15.4", "1979"], ["400", "36", "4", "135.0", "84", "2370", "13.0", "1982"], ["135", "19", "6", "232.0", "100", "2901", "16.0", "1974"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["146", "14", "8", "318.0", "150", "4457", "13.5", "1974"], ["57", "23", "4", "122.0", "86", "2220", "14.0", "1971"], ["52", "13", "8", "400.0", "175", "5140", "12.0", "1971"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["172", "21", "6", "231.0", "110", "3039", "15.0", "1975"], ["164", "16", "8", "400.0", "170", "4668", "11.5", "1975"], ["179", "24", "4", "134.0", "96", "2702", "13.5", "1975"], ["200", "22", "6", "250.0", "105", "3353", "14.5", "1976"], ["385", "37", "4", "91.0", "68", "2025", "18.2", "1982"], ["166", "16", "8", "318.0", "150", "4498", "14.5", "1975"], ["396", "38", "6", "262.0", "85", "3015", "17.0", "1982"], ["397", "26", "4", "156.0", "92", "2585", "14.5", "1982"], ["232", "15", "8", "302.0", "130", "4295", "14.9", "1977"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["147", "14", "8", "302.0", "140", "4638", "16.0", "1974"], ["201", "24", "6", "200.0", "81", "3012", "17.6", "1976"], ["390", "36", "4", "107.0", "75", "2205", "14.5", "1982"], ["169", "16", "6", "250.0", "105", "3897", "18.5", "1975"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["401", "27", "4", "151.0", "90", "2950", "17.3", "1982"], ["80", "15", "8", "304.0", "150", "3892", "12.5", "1972"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["168", "17", "6", "231.0", "110", "3907", "21.0", "1975"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["290", "22.3", "4", "140.0", "88", "2890", "17.3", "1979"], ["398", "22", "6", "232.0", "112", "2835", "14.7", "1982"], ["77", "12", "8", "350.0", "160", "4456", "13.5", "1972"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the maximum accelerate for all the different cylinders?	[["13.5", "3"], ["24.8", "4"], ["20.1", "5"], ["21.0", "6"], ["22.2", "8"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["173", "chevrolet", "chevrolet monza 2+2"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["293", "chevrolet", "chevrolet caprice classic"], ["343", "triumph", "triumph tr7 coupe"], ["82", "ford", "ford gran torino (sw)"], ["229", "chevrolet", "chevrolet caprice classic"], ["241", "volkswagen", "volkswagen rabbit custom"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["87", "renault", "renault 12 (sw)"], ["285", "peugeot", "peugeot 604sl"], ["216", "plymouth", "plymouth volare premier v8"], ["205", "volkswagen", "vw rabbit"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["141", "chevrolet", "chevrolet chevelle malibu classic"], ["338", "renault", "renault lecar deluxe"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["165", "chevrolet", "chevrolet bel air"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["371", "datsun", "datsun 810 maxima"], ["27", "peugeot", "peugeot 504"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["242", "pontiac", "pontiac sunbird coupe"], ["110", "volkswagen", "volkswagen super beetle"], ["106", "chevrolet", "chevrolet nova custom"], ["301", "volkswagen", "vw rabbit custom"], ["147", "ford", "ford gran torino (sw)"], ["99", "chevrolet", "chevrolet caprice classic"], ["340", " volkswagen", "volkswagen rabbit"], ["245", "chevrolet", "chevrolet chevette"], ["356", "toyota", "toyota tercel"], ["200", "chevrolet", "chevrolet nova"], ["307", "peugeot", "peugeot 504"], ["179", "toyota", "toyota corona"], ["362", "renault", "renault 18i"], ["286", "volkswagen", "volkswagen scirocco"], ["86", "peugeot", "peugeot 504 (sw)"], ["50", "dodge", "dodge monaco (sw)"], ["178", "pontiac", "pontiac astro"], ["218", "toyota", "toyota mark ii"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["180", "volkswagen", "volkswagen dasher"], ["317", "volkswagen", "vw rabbit"], ["370", "toyota", "toyota cressida"], ["67", "volkswagen", "volkswagen type 3"], ["63", "volkswagen", "volkswagen model 111"], ["101", "plymouth", "plymouth fury gran sedan"], ["131", "toyota", "toyota mark ii"], ["90", "toyota", "toyota corona mark ii (sw)"], ["217", "peugeot", "peugeot 504"], ["211", "volkswagen", "volkswagen rabbit"], ["19", "chevrolet", "chevrolet monte carlo"], ["334", "volkswagen", "vw dasher (diesel)"], ["312", "fiat", "fiat strada custom"], ["384", "volkswagen", "volkswagen rabbit l"], ["122", "fiat", "fiat 124 sport coupe"], ["161", "chevrolet", "chevrolet nova"], ["299", "chevrolet", "chevrolet malibu classic (sw)"], ["40", "volkswagen", "volkswagen super beetle 117"], ["204", "chevrolet", "chevrolet woody"], ["54", "chevrolet", "chevrolet vega (sw)"], ["226", "renault", "renault 5 gtl"], ["21", "toyota", "toyota corona mark ii"], ["144", "ford", "ford gran torino"], ["183", "volkswagen", "volkswagen rabbit"], ["335", "audi", "audi 5000s (diesel)"], ["186", "peugeot", "peugeot 504"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["239", "chrysler", "chrysler cordoba"], ["382", "ford", "ford fairmont futura"], ["116", "toyota", "toyota carina"], ["30", "bmw", "bmw 2002"], ["262", "ford", "ford fairmont (auto)"], ["231", "dodge", "dodge monaco brougham"], ["52", "pontiac", "pontiac safari (sw)"], ["305", "mercedes", "mercedes benz 300d"], ["288", "pontiac", "pontiac lemans v6"], ["123", "chevrolet", "chevrolet monte carlo s"], ["363", "honda", "honda prelude"], ["208", "ford", "ford granada ghia"], ["59", "peugeot", "peugeot 304"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["244", "ford", "ford mustang ii 2+2"], ["328", "datsun", "datsun 510 hatchback"], ["275", "toyota", "toyota corona"], ["43", "chevrolet", "chevrolet chevelle malibu"], ["376", "chevrolet", "chevrolet cavalier"], ["203", "chevrolet", "chevrolet chevette"], ["228", "datsun", "datsun f-10 hatchback"], ["319", "chevrolet", "chevrolet chevette"], ["152", "toyota", "toyota corona"], ["198", "ford", "ford gran torino"], ["7", "chevrolet", "chevrolet impala"], ["318", "toyota", "toyota corolla tercel"], ["96", "ford", "ford gran torino"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["278", "toyota", "toyota celica gt liftback"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["209", "pontiac", "pontiac ventura sj"], ["150", "volkswagen", "volkswagen dasher"], ["117", "chevrolet", "chevrolet vega"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["140", "chevrolet", "chevrolet vega"], ["70", "chevrolet", "chevrolet impala"], ["136", "chevrolet", "chevrolet nova"], ["361", "volkswagen", "volkswagen jetta"], ["159", "fiat", "fiat x1.9"], ["389", "nissan", "nissan stanza xe"], ["271", "buick", "buick regal sport coupe (turbo)"], ["111", "chevrolet", "chevrolet impala"], ["46", "chevrolet", "chevrolet impala"], ["185", "audi", "audi 100ls"], ["191", "opel", "opel 1900"], ["89", "datsun", "datsun 510 (sw)"], ["62", "datsun", "datsun 1200"], ["169", "chevrolet", "chevrolet chevelle malibu"], ["355", "datsun", "datsun 210 mpg"], ["225", "buick", "buick opel isuzu deluxe"], ["243", "toyota", "toyota corolla liftback"], ["332", "datsun", "datsun 210"], ["73", "ford", "ford galaxie 500"], ["297", "buick", "buick estate wagon (sw)"], ["311", "datsun", "datsun 210"], ["95", "chevrolet", "chevrolet malibu"], ["381", "pontiac", "pontiac phoenix"], ["233", "chevrolet", "chevrolet concours"], ["260", "pontiac", "pontiac phoenix lj"], ["194", "renault", "renault 12tl"], ["164", "pontiac", "pontiac catalina"], ["68", "chevrolet", "chevrolet vega"], ["316", "pontiac", "pontiac phoenix"], ["221", "chevrolet", "chevy c10"], ["393", "honda", "honda civic (auto)"], ["153", "datsun", "datsun 710"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["394", "datsun", "datsun 310 gx"], ["78", "chrysler", "chrysler newport royal"], ["399", "toyota", "toyota celica gt"], ["13", "ford", "ford torino (sw)"], ["325", "audi", "audi 4000"], ["58", "opel", "opel 1900"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Which model has the most version(make) of cars?	[["ford"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["371", "datsun", "datsun 810 maxima"], ["148", "amc", "amc matador (sw)"], ["173", "chevrolet", "chevrolet monza 2+2"], ["212", "datsun", "datsun b-210"], ["191", "opel", "opel 1900"], ["160", "plymouth", "plymouth valiant custom"], ["80", "amc", "amc matador (sw)"], ["310", "plymouth", "plymouth horizon tc3"], ["87", "renault", "renault 12 (sw)"], ["159", "fiat", "fiat x1.9"], ["89", "datsun", "datsun 510 (sw)"], ["350", "plymouth", "plymouth reliant"], ["137", "datsun", "datsun b210"], ["338", "renault", "renault lecar deluxe"], ["216", "plymouth", "plymouth volare premier v8"], ["153", "datsun", "datsun 710"], ["311", "datsun", "datsun 210"], ["343", "triumph", "triumph tr7 coupe"], ["151", "opel", "opel manta"], ["62", "datsun", "datsun 1200"], ["285", "peugeot", "peugeot 604sl"], ["42", "plymouth", "plymouth satellite custom"], ["394", "datsun", "datsun 310 gx"], ["332", "datsun", "datsun 210"], ["126", "opel", "opel manta"], ["235", "plymouth", "plymouth volare custom"], ["380", "dodge", "dodge aries se"], ["58", "opel", "opel 1900"], ["72", "plymouth", "plymouth fury iii"], ["199", "plymouth", "plymouth valiant"], ["121", "mercury", "mercury capri v6"], ["83", "plymouth", "plymouth satellite custom (sw)"], ["15", "amc", "amc rebel sst (sw)"], ["105", "plymouth", "plymouth valiant"], ["207", "dodge", "dodge aspen se"], ["36", "datsun", "datsun pl510"], ["365", "datsun", "datsun 200sx"], ["293", "chevrolet", "chevrolet caprice classic"], ["25", "datsun", "datsun pl510"], ["370", "toyota", "toyota cressida"], ["255", "datsun", "datsun b210 gx"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["241", "volkswagen", "volkswagen rabbit custom"], ["358", "plymouth", "plymouth horizon 4"], ["249", "datsun", "datsun 810"], ["50", "dodge", "dodge monaco (sw)"], ["142", "amc", "amc matador"], ["54", "chevrolet", "chevrolet vega (sw)"], ["197", "amc", "amc matador"], ["229", "chevrolet", "chevrolet caprice classic"], ["304", "amc", "amc spirit dl"], ["340", " volkswagen", "volkswagen rabbit"], ["231", "dodge", "dodge monaco brougham"], ["170", "amc", "amc matador"], ["63", "volkswagen", "volkswagen model 111"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["94", "amc", "amc matador"], ["320", "datsun", "datsun 310"], ["362", "renault", "renault 18i"], ["305", "mercedes", "mercedes benz 300d"], ["11", "citroen", "citroen ds-21 pallas"], ["210", "amc", "amc pacer d/l"], ["8", "plymouth", "plymouth fury iii"], ["273", "dodge", "dodge magnum xe"], ["118", "datsun", "datsun 610"], ["276", "datsun", "datsun 510"], ["301", "volkswagen", "vw rabbit custom"], ["346", "plymouth", "plymouth reliant"], ["363", "honda", "honda prelude"], ["299", "chevrolet", "chevrolet malibu classic (sw)"], ["205", "volkswagen", "vw rabbit"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["184", "amc", "amc pacer"], ["45", "amc", "amc matador"], ["161", "chevrolet", "chevrolet nova"], ["225", "buick", "buick opel isuzu deluxe"], ["166", "plymouth", "plymouth grand fury"], ["211", "volkswagen", "volkswagen rabbit"], ["178", "pontiac", "pontiac astro"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["37", "chevrolet", "chevrolet vega 2300"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["245", "chevrolet", "chevrolet chevette"], ["383", "amc", "amc concord dl"], ["49", "plymouth", "plymouth fury iii"], ["284", "saab", "saab 99gle"], ["141", "chevrolet", "chevrolet chevelle malibu classic"], ["307", "peugeot", "peugeot 504"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["53", "amc", "amc hornet sportabout (sw)"], ["200", "chevrolet", "chevrolet nova"], ["181", "datsun", "datsun 710"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["86", "peugeot", "peugeot 504 (sw)"], ["14", "plymouth", "plymouth satellite (sw)"], ["382", "ford", "ford fairmont futura"], ["52", "pontiac", "pontiac safari (sw)"], ["291", "amc", "amc concord dl 6"], ["143", "plymouth", "plymouth satellite sebring"], ["82", "ford", "ford gran torino (sw)"], ["384", "volkswagen", "volkswagen rabbit l"], ["27", "peugeot", "peugeot 504"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["67", "volkswagen", "volkswagen type 3"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["308", "oldsmobile", "oldsmobile cutlass salon brougham"], ["341", "datsun", "datsun 280-zx"], ["3", "plymouth", "plymouth satellite"], ["258", "dodge", "dodge diplomat"], ["101", "plymouth", "plymouth fury gran sedan"], ["30", "bmw", "bmw 2002"], ["179", "toyota", "toyota corona"], ["106", "chevrolet", "chevrolet nova custom"], ["4", "amc", "amc rebel sst"], ["220", "cadillac", "cadillac seville"], ["99", "chevrolet", "chevrolet caprice classic"], ["356", "toyota", "toyota tercel"], ["368", "saab", "saab 900s"], ["183", "volkswagen", "volkswagen rabbit"], ["400", "dodge", "dodge charger 2.2"], ["226", "renault", "renault 5 gtl"], ["204", "chevrolet", "chevrolet woody"], ["218", "toyota", "toyota mark ii"], ["286", "volkswagen", "volkswagen scirocco"], ["22", "plymouth", "plymouth duster"], ["389", "nissan", "nissan stanza xe"], ["164", "pontiac", "pontiac catalina"], ["244", "ford", "ford mustang ii 2+2"], ["165", "chevrolet", "chevrolet bel air"], ["90", "toyota", "toyota corona mark ii (sw)"], ["203", "chevrolet", "chevrolet chevette"], ["117", "chevrolet", "chevrolet vega"], ["133", "plymouth", "plymouth duster"], ["140", "chevrolet", "chevrolet vega"], ["177", "amc", "amc gremlin"], ["355", "datsun", "datsun 210 mpg"], ["292", "dodge", "dodge aspen 6"], ["180", "volkswagen", "volkswagen dasher"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What model has the most different versions?	[["ford"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["156", "26", "4", "116.0", "75", "2246", "14.0", "1974"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["185", "23", "4", "115.0", "95", "2694", "15.0", "1975"], ["125", "29", "4", "68.0", "49", "1867", "19.5", "1973"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["138", "26", "4", "122.0", "80", "2451", "16.5", "1974"], ["4", "16", "8", "304.0", "150", "3433", "12.0", "1970"], ["146", "14", "8", "318.0", "150", "4457", "13.5", "1974"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["192", "25", "4", "140.0", "92", "2572", "14.9", "1976"], ["147", "14", "8", "302.0", "140", "4638", "16.0", "1974"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["57", "23", "4", "122.0", "86", "2220", "14.0", "1971"], ["126", "24", "4", "116.0", "75", "2158", "15.5", "1973"], ["179", "24", "4", "134.0", "96", "2702", "13.5", "1975"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["397", "26", "4", "156.0", "92", "2585", "14.5", "1982"], ["127", "20", "4", "114.0", "91", "2582", "14.0", "1973"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["350", "30", "4", "135.0", "84", "2385", "12.9", "1981"], ["26", "26", "4", "97.0", "46", "1835", "20.5", "1970"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["154", "28", "4", "90.0", "75", "2125", "14.5", "1974"], ["152", "31", "4", "76.0", "52", "1649", "16.5", "1974"], ["176", "23", "4", "140.0", "83", "2639", "17.0", "1975"], ["136", "15", "6", "250.0", "100", "3336", "17.0", "1974"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["108", "18", "6", "250.0", "88", "3021", "16.5", "1973"], ["382", "24", "4", "140.0", "92", "2865", "16.4", "1982"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["206", "33", "4", "91.0", "53", "1795", "17.4", "1976"], ["84", "18", "4", "121.0", "112", "2933", "14.5", "1972"], ["121", "21", "6", "155.0", "107", "2472", "14.0", "1973"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["128", "19", "4", "121.0", "112", "2868", "15.5", "1973"], ["130", "24", "4", "121.0", "110", "2660", "14.0", "1973"], ["54", "22", "4", "140.0", "72", "2408", "19.0", "1971"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"], ["150", "26", "4", "79.0", "67", "1963", "15.5", "1974"], ["186", "23", "4", "120.0", "88", "2957", "17.0", "1975"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"], ["139", "32", "4", "71.0", "65", "1836", "21.0", "1974"], ["69", "21", "4", "122.0", "86", "2226", "16.5", "1972"], ["124", "16", "8", "400.0", "230", "4278", "9.5", "1973"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["298", "15.5", "8", "351.0", "142", "4054", "14.3", "1979"], ["157", "24", "4", "120.0", "97", "2489", "15.0", "1974"], ["116", "20", "4", "97.0", "88", "2279", "19.0", "1973"], ["385", "37", "4", "91.0", "68", "2025", "18.2", "1982"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["244", "25.5", "4", "140.0", "89", "2755", "15.8", "1977"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["62", "35", "4", "72.0", "69", "1613", "18.0", "1971"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["232", "15", "8", "302.0", "130", "4295", "14.9", "1977"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:How many cars have more than 4 cylinders?	[["195"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["4", "16", "8", "304.0", "150", "3433", "12.0", "1970"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["156", "26", "4", "116.0", "75", "2246", "14.0", "1974"], ["185", "23", "4", "115.0", "95", "2694", "15.0", "1975"], ["125", "29", "4", "68.0", "49", "1867", "19.5", "1973"], ["146", "14", "8", "318.0", "150", "4457", "13.5", "1974"], ["147", "14", "8", "302.0", "140", "4638", "16.0", "1974"], ["138", "26", "4", "122.0", "80", "2451", "16.5", "1974"], ["57", "23", "4", "122.0", "86", "2220", "14.0", "1971"], ["126", "24", "4", "116.0", "75", "2158", "15.5", "1973"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["130", "24", "4", "121.0", "110", "2660", "14.0", "1973"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["397", "26", "4", "156.0", "92", "2585", "14.5", "1982"], ["192", "25", "4", "140.0", "92", "2572", "14.9", "1976"], ["154", "28", "4", "90.0", "75", "2125", "14.5", "1974"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["179", "24", "4", "134.0", "96", "2702", "13.5", "1975"], ["176", "23", "4", "140.0", "83", "2639", "17.0", "1975"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["54", "22", "4", "140.0", "72", "2408", "19.0", "1971"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["84", "18", "4", "121.0", "112", "2933", "14.5", "1972"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"], ["128", "19", "4", "121.0", "112", "2868", "15.5", "1973"], ["152", "31", "4", "76.0", "52", "1649", "16.5", "1974"], ["382", "24", "4", "140.0", "92", "2865", "16.4", "1982"], ["127", "20", "4", "114.0", "91", "2582", "14.0", "1973"], ["385", "37", "4", "91.0", "68", "2025", "18.2", "1982"], ["26", "26", "4", "97.0", "46", "1835", "20.5", "1970"], ["108", "18", "6", "250.0", "88", "3021", "16.5", "1973"], ["139", "32", "4", "71.0", "65", "1836", "21.0", "1974"], ["206", "33", "4", "91.0", "53", "1795", "17.4", "1976"], ["136", "15", "6", "250.0", "100", "3336", "17.0", "1974"], ["157", "24", "4", "120.0", "97", "2489", "15.0", "1974"], ["350", "30", "4", "135.0", "84", "2385", "12.9", "1981"], ["186", "23", "4", "120.0", "88", "2957", "17.0", "1975"], ["121", "21", "6", "155.0", "107", "2472", "14.0", "1973"], ["150", "26", "4", "79.0", "67", "1963", "15.5", "1974"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["244", "25.5", "4", "140.0", "89", "2755", "15.8", "1977"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["69", "21", "4", "122.0", "86", "2226", "16.5", "1972"], ["304", "27.4", "4", "121.0", "80", "2670", "15.0", "1979"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["124", "16", "8", "400.0", "230", "4278", "9.5", "1973"], ["37", "28", "4", "140.0", "90", "2264", "15.5", "1971"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["96", "14", "8", "302.0", "137", "4042", "14.5", "1973"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["232", "15", "8", "302.0", "130", "4295", "14.9", "1977"], ["116", "20", "4", "97.0", "88", "2279", "19.0", "1973"], ["88", "22", "4", "122.0", "86", "2395", "16.0", "1972"], ["399", "32", "4", "144.0", "96", "2665", "13.9", "1982"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the number of cars with more than 4 cylinders?	[["195"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["327", "31.3", "4", "120.0", "75", "2542", "17.5", "1980"], ["318", "38.1", "4", "89.0", "60", "1968", "18.8", "1980"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["325", "34.3", "4", "97.0", "78", "2188", "15.8", "1980"], ["340", "29.8", "4", "89.0", "62", "1845", "15.3", "1980"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"], ["341", "32.7", "6", "168.0", "132", "2910", "11.4", "1980"], ["326", "29.8", "4", "134.0", "90", "2711", "15.5", "1980"], ["317", "41.5", "4", "98.0", "76", "2144", "14.7", "1980"], ["293", "17", "8", "305.0", "130", "3840", "15.4", "1979"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["296", "18.2", "8", "318.0", "135", "3830", "15.2", "1979"], ["84", "18", "4", "121.0", "112", "2933", "14.5", "1972"], ["322", "26.4", "4", "140.0", "88", "2870", "18.1", "1980"], ["106", "16", "6", "250.0", "100", "3278", "18.0", "1973"], ["217", "19", "4", "120.0", "88", "3270", "21.9", "1976"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["334", "43.4", "4", "90.0", "48", "2335", "23.7", "1980"], ["209", "18.5", "6", "250.0", "110", "3645", "16.2", "1976"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["219", "16.5", "6", "168.0", "120", "3820", "16.7", "1976"], ["382", "24", "4", "140.0", "92", "2865", "16.4", "1982"], ["171", "18", "6", "225.0", "95", "3785", "19.0", "1975"], ["337", "44.6", "4", "91.0", "67", "1850", "13.8", "1980"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["395", "25", "6", "181.0", "110", "2945", "16.4", "1982"], ["229", "17.5", "8", "305.0", "145", "3880", "12.5", "1977"], ["285", "16.2", "6", "163.0", "133", "3410", "15.8", "1978"], ["319", "32.1", "4", "98.0", "70", "2120", "15.5", "1980"], ["324", "19.1", "6", "225.0", "90", "3381", "18.7", "1980"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["330", "46.6", "4", "86.0", "65", "2110", "17.9", "1980"], ["321", "28", "4", "151.0", "90", "2678", "16.5", "1980"], ["225", "30", "4", "111.0", "80", "2155", "14.8", "1977"], ["16", "15", "8", "383.0", "170", "3563", "10.0", "1970"], ["379", "31", "4", "112.0", "85", "2575", "16.2", "1982"], ["169", "16", "6", "250.0", "105", "3897", "18.5", "1975"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["108", "18", "6", "250.0", "88", "3021", "16.5", "1973"], ["402", "27", "4", "140.0", "86", "2790", "15.6", "1982"], ["297", "16.9", "8", "350.0", "155", "4360", "14.9", "1979"], ["332", "40.8", "4", "85.0", "65", "2110", "19.2", "1980"], ["254", "32.8", "4", "78.0", "52", "1985", "19.4", "1978"], ["385", "37", "4", "91.0", "68", "2025", "18.2", "1982"], ["24", "21", "6", "200.0", "85", "2587", "16.0", "1970"], ["386", "31", "4", "91.0", "68", "1970", "17.6", "1982"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["121", "21", "6", "155.0", "107", "2472", "14.0", "1973"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["69", "21", "4", "122.0", "86", "2226", "16.5", "1972"], ["352", "39", "4", "86.0", "64", "1875", "16.4", "1981"], ["94", "14", "8", "304.0", "150", "3672", "11.5", "1973"], ["397", "26", "4", "156.0", "92", "2585", "14.5", "1982"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["357", "34.1", "4", "91.0", "68", "1985", "16.0", "1981"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["80", "15", "8", "304.0", "150", "3892", "12.5", "1972"], ["240", "16", "8", "351.0", "149", "4335", "14.5", "1977"], ["88", "22", "4", "122.0", "86", "2395", "16.0", "1972"], ["396", "38", "6", "262.0", "85", "3015", "17.0", "1982"], ["335", "36.4", "5", "121.0", "67", "2950", "19.9", "1980"], ["375", "17.6", "6", "225.0", "85", "3465", "16.6", "1981"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:how many cars were produced in 1980?	[["29"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["318", "38.1", "4", "89.0", "60", "1968", "18.8", "1980"], ["327", "31.3", "4", "120.0", "75", "2542", "17.5", "1980"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["340", "29.8", "4", "89.0", "62", "1845", "15.3", "1980"], ["325", "34.3", "4", "97.0", "78", "2188", "15.8", "1980"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["341", "32.7", "6", "168.0", "132", "2910", "11.4", "1980"], ["326", "29.8", "4", "134.0", "90", "2711", "15.5", "1980"], ["317", "41.5", "4", "98.0", "76", "2144", "14.7", "1980"], ["84", "18", "4", "121.0", "112", "2933", "14.5", "1972"], ["296", "18.2", "8", "318.0", "135", "3830", "15.2", "1979"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["217", "19", "4", "120.0", "88", "3270", "21.9", "1976"], ["322", "26.4", "4", "140.0", "88", "2870", "18.1", "1980"], ["293", "17", "8", "305.0", "130", "3840", "15.4", "1979"], ["106", "16", "6", "250.0", "100", "3278", "18.0", "1973"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["171", "18", "6", "225.0", "95", "3785", "19.0", "1975"], ["219", "16.5", "6", "168.0", "120", "3820", "16.7", "1976"], ["334", "43.4", "4", "90.0", "48", "2335", "23.7", "1980"], ["209", "18.5", "6", "250.0", "110", "3645", "16.2", "1976"], ["382", "24", "4", "140.0", "92", "2865", "16.4", "1982"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["395", "25", "6", "181.0", "110", "2945", "16.4", "1982"], ["319", "32.1", "4", "98.0", "70", "2120", "15.5", "1980"], ["324", "19.1", "6", "225.0", "90", "3381", "18.7", "1980"], ["337", "44.6", "4", "91.0", "67", "1850", "13.8", "1980"], ["169", "16", "6", "250.0", "105", "3897", "18.5", "1975"], ["321", "28", "4", "151.0", "90", "2678", "16.5", "1980"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["16", "15", "8", "383.0", "170", "3563", "10.0", "1970"], ["379", "31", "4", "112.0", "85", "2575", "16.2", "1982"], ["386", "31", "4", "91.0", "68", "1970", "17.6", "1982"], ["225", "30", "4", "111.0", "80", "2155", "14.8", "1977"], ["330", "46.6", "4", "86.0", "65", "2110", "17.9", "1980"], ["285", "16.2", "6", "163.0", "133", "3410", "15.8", "1978"], ["69", "21", "4", "122.0", "86", "2226", "16.5", "1972"], ["108", "18", "6", "250.0", "88", "3021", "16.5", "1973"], ["254", "32.8", "4", "78.0", "52", "1985", "19.4", "1978"], ["385", "37", "4", "91.0", "68", "2025", "18.2", "1982"], ["384", "36", "4", "105.0", "74", "1980", "15.3", "1982"], ["402", "27", "4", "140.0", "86", "2790", "15.6", "1982"], ["229", "17.5", "8", "305.0", "145", "3880", "12.5", "1977"], ["357", "34.1", "4", "91.0", "68", "1985", "16.0", "1981"], ["332", "40.8", "4", "85.0", "65", "2110", "19.2", "1980"], ["24", "21", "6", "200.0", "85", "2587", "16.0", "1970"], ["352", "39", "4", "86.0", "64", "1875", "16.4", "1981"], ["335", "36.4", "5", "121.0", "67", "2950", "19.9", "1980"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["121", "21", "6", "155.0", "107", "2472", "14.0", "1973"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["94", "14", "8", "304.0", "150", "3672", "11.5", "1973"], ["375", "17.6", "6", "225.0", "85", "3465", "16.6", "1981"], ["297", "16.9", "8", "350.0", "155", "4360", "14.9", "1979"], ["396", "38", "6", "262.0", "85", "3015", "17.0", "1982"], ["397", "26", "4", "156.0", "92", "2585", "14.5", "1982"], ["80", "15", "8", "304.0", "150", "3892", "12.5", "1972"], ["398", "22", "6", "232.0", "112", "2835", "14.7", "1982"], ["88", "22", "4", "122.0", "86", "2395", "16.0", "1972"], ["303", "35.7", "4", "98.0", "80", "1915", "14.4", "1979"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:In 1980, how many cars were made?	[["29"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"], ["22", "kia", "Kia Motors", "8"], ["4", "gm", "General Motors", "1"], ["8", "nissan", "Nissan Motors", "4"], ["5", "ford", "Ford Motor Company", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["8", "6", "chrysler"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["7", "4", "chevrolet"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["6", "chrysler", "Chrysler", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"], ["20", "8", "nissan"], ["29", "19", "toyota"], ["25", "4", "pontiac"], ["23", "15", "peugeot"], ["26", "16", "renault"], ["18", "13", "mercedes-benz"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["23", "hyundai", "Hyundai", "8"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["22", "14", "opel"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["2", "volkswagen", "Volkswagen", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["21", "4", "oldsmobile"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["19", "toyota", "Toyota", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["34", "23", "hyundai"], ["32", "21", "volvo"], ["35", "6", "jeep"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["21", "volvo", "Volvo", "6"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["3", "3", "bmw"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["3", "bmw", "BMW", "2"], ["16", "renault", "Renault", "3"], ["14", "opel", "Opel", "2"], ["7", "citroen", "Citroen", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["13", "5", "ford"], ["4", "4", "buick"], ["16", "12", "mazda"], ["28", "18", "subaru"], ["9", "7", "citroen"], ["5", "4", "cadillac"], ["33", "22", "kia"], ["17", "13", "mercedes"], ["15", "11", "honda"], ["2", "2", "audi"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["18", "subaru", "Subaru", "4"], ["12", "mazda", "Mazda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["10", "8", "datsun"], ["27", "17", "saab"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["12", "9", "fiat"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["11", "honda", "Honda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["1", "1", "amc"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["11", "6", "dodge"], ["24", "6", "plymouth"], ["19", "5", "mercury"], ["36", "19", "scion"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["30", "20", "triumph"], ["6", "5", "capri"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["14", "10", "hi"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:How many car models were produced by the maker with full name American Motor Company?	[["1"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"], ["22", "kia", "Kia Motors", "8"], ["8", "nissan", "Nissan Motors", "4"], ["13", "daimler benz", "Daimler Benz", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["8", "6", "chrysler"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["7", "4", "chevrolet"], ["31", "2", "volkswagen"], ["20", "8", "nissan"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["5", "ford", "Ford Motor Company", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["23", "15", "peugeot"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["6", "chrysler", "Chrysler", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["25", "4", "pontiac"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["23", "hyundai", "Hyundai", "8"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["26", "16", "renault"], ["29", "19", "toyota"], ["18", "13", "mercedes-benz"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["2", "volkswagen", "Volkswagen", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["34", "23", "hyundai"], ["35", "6", "jeep"], ["22", "14", "opel"], ["32", "21", "volvo"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["21", "volvo", "Volvo", "6"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["21", "4", "oldsmobile"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["19", "toyota", "Toyota", "4"], ["16", "renault", "Renault", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["13", "5", "ford"], ["3", "3", "bmw"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["3", "bmw", "BMW", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["28", "18", "subaru"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["18", "subaru", "Subaru", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["16", "12", "mazda"], ["33", "22", "kia"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["7", "citroen", "Citroen", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["17", "13", "mercedes"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["15", "11", "honda"], ["4", "4", "buick"], ["9", "7", "citroen"], ["2", "2", "audi"], ["12", "9", "fiat"], ["5", "4", "cadillac"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["10", "8", "datsun"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["27", "17", "saab"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["11", "honda", "Honda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["11", "6", "dodge"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["24", "6", "plymouth"], ["1", "1", "amc"], ["19", "5", "mercury"], ["36", "19", "scion"], ["6", "5", "capri"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["30", "20", "triumph"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["14", "10", "hi"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the number of car models created by the car maker American Motor Company?	[["1"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"], ["8", "nissan", "Nissan Motors", "4"], ["22", "kia", "Kia Motors", "8"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["16", "renault", "Renault", "3"], ["1", "amc", "American Motor Company", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["3", "3", "bmw"], ["23", "15", "peugeot"], ["26", "16", "renault"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["2", "volkswagen", "Volkswagen", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["7", "4", "chevrolet"], ["18", "13", "mercedes-benz"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"], ["3", "bmw", "BMW", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["20", "8", "nissan"], ["29", "19", "toyota"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["21", "volvo", "Volvo", "6"], ["7", "citroen", "Citroen", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["32", "21", "volvo"], ["22", "14", "opel"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"], ["5", "ford", "Ford Motor Company", "1"], ["23", "hyundai", "Hyundai", "8"], ["19", "toyota", "Toyota", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["8", "6", "chrysler"], ["34", "23", "hyundai"], ["25", "4", "pontiac"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["6", "chrysler", "Chrysler", "1"], ["18", "subaru", "Subaru", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["15", "11", "honda"], ["2", "2", "audi"], ["28", "18", "subaru"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["11", "honda", "Honda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["17", "13", "mercedes"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["16", "12", "mazda"], ["21", "4", "oldsmobile"], ["33", "22", "kia"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["35", "6", "jeep"], ["4", "4", "buick"], ["9", "7", "citroen"], ["12", "9", "fiat"], ["13", "5", "ford"], ["10", "8", "datsun"], ["5", "4", "cadillac"], ["27", "17", "saab"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["24", "6", "plymouth"], ["1", "1", "amc"], ["11", "6", "dodge"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"], ["20", "triumph", "Triumph", "7"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["36", "19", "scion"], ["6", "5", "capri"], ["30", "20", "triumph"], ["19", "5", "mercury"], ["14", "10", "hi"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Which makers designed more than 3 car models? List full name and the id.	[["General Motors", "4"], ["Chrysler", "6"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"], ["16", "renault", "Renault", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"], ["26", "16", "renault"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["32", "21", "volvo"], ["23", "15", "peugeot"], ["3", "3", "bmw"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"], ["8", "nissan", "Nissan Motors", "4"], ["22", "kia", "Kia Motors", "8"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["22", "14", "opel"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["2", "volkswagen", "Volkswagen", "2"], ["7", "citroen", "Citroen", "3"], ["21", "volvo", "Volvo", "6"], ["1", "amc", "American Motor Company", "1"], ["3", "bmw", "BMW", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["7", "4", "chevrolet"], ["29", "19", "toyota"], ["34", "23", "hyundai"], ["20", "8", "nissan"], ["27", "17", "saab"], ["25", "4", "pontiac"], ["2", "2", "audi"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["18", "subaru", "Subaru", "4"], ["5", "ford", "Ford Motor Company", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["21", "4", "oldsmobile"], ["18", "13", "mercedes-benz"], ["33", "22", "kia"], ["28", "18", "subaru"], ["15", "11", "honda"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["19", "toyota", "Toyota", "4"], ["23", "hyundai", "Hyundai", "8"], ["6", "chrysler", "Chrysler", "1"], ["11", "honda", "Honda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["8", "6", "chrysler"], ["9", "7", "citroen"], ["10", "8", "datsun"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["17", "13", "mercedes"], ["12", "9", "fiat"], ["24", "6", "plymouth"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"], ["15", "peugeaut", "Peugeaut", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["35", "6", "jeep"], ["4", "4", "buick"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["16", "12", "mazda"], ["5", "4", "cadillac"], ["13", "5", "ford"], ["1", "1", "amc"], ["36", "19", "scion"], ["11", "6", "dodge"], ["6", "5", "capri"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["30", "20", "triumph"], ["19", "5", "mercury"], ["14", "10", "hi"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the names and ids of all makers with more than 3 models?	[["General Motors", "4"], ["Chrysler", "6"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["221", "chevrolet", "chevy c10"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["350", "plymouth", "plymouth reliant"], ["93", "buick", "buick century 350"], ["293", "chevrolet", "chevrolet caprice classic"], ["245", "chevrolet", "chevrolet chevette"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["95", "chevrolet", "chevrolet malibu"], ["33", "chevrolet", "chevy c20"], ["200", "chevrolet", "chevrolet nova"], ["299", "chevrolet", "chevrolet malibu classic (sw)"], ["173", "chevrolet", "chevrolet monza 2+2"], ["106", "chevrolet", "chevrolet nova custom"], ["349", "chevrolet", "chevrolet citation"], ["233", "chevrolet", "chevrolet concours"], ["117", "chevrolet", "chevrolet vega"], ["301", "volkswagen", "vw rabbit custom"], ["229", "chevrolet", "chevrolet caprice classic"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["123", "chevrolet", "chevrolet monte carlo s"], ["145", "buick", "buick century luxus (sw)"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["161", "chevrolet", "chevrolet nova"], ["43", "chevrolet", "chevrolet chevelle malibu"], ["288", "pontiac", "pontiac lemans v6"], ["136", "chevrolet", "chevrolet nova"], ["141", "chevrolet", "chevrolet chevelle malibu classic"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["124", "pontiac", "pontiac grand prix"], ["376", "chevrolet", "chevrolet cavalier"], ["203", "chevrolet", "chevrolet chevette"], ["334", "volkswagen", "vw dasher (diesel)"], ["321", "chevrolet", "chevrolet citation"], ["73", "ford", "ford galaxie 500"], ["63", "volkswagen", "volkswagen model 111"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["99", "chevrolet", "chevrolet caprice classic"], ["314", "chevrolet", "chevrolet citation"], ["262", "ford", "ford fairmont (auto)"], ["165", "chevrolet", "chevrolet bel air"], ["140", "chevrolet", "chevrolet vega"], ["19", "chevrolet", "chevrolet monte carlo"], ["319", "chevrolet", "chevrolet chevette"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["237", "pontiac", "pontiac grand prix lj"], ["82", "ford", "ford gran torino (sw)"], ["241", "volkswagen", "volkswagen rabbit custom"], ["37", "chevrolet", "chevrolet vega 2300"], ["374", "ford", "ford granada gl"], ["274", "chevrolet", "chevrolet chevette"], ["261", "chevrolet", "chevrolet malibu"], ["204", "chevrolet", "chevrolet woody"], ["225", "buick", "buick opel isuzu deluxe"], ["147", "ford", "ford gran torino (sw)"], ["379", "pontiac", "pontiac j2000 se hatchback"], ["103", "buick", "buick electra 225 custom"], ["340", " volkswagen", "volkswagen rabbit"], ["294", "ford", "ford ltd landau"], ["406", "chevrolet", "chevy s-10"], ["205", "volkswagen", "vw rabbit"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["208", "ford", "ford granada ghia"], ["359", "ford", "ford escort 4w"], ["144", "ford", "ford gran torino"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["297", "buick", "buick estate wagon (sw)"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["178", "pontiac", "pontiac astro"], ["335", "audi", "audi 5000s (diesel)"], ["54", "chevrolet", "chevrolet vega (sw)"], ["67", "volkswagen", "volkswagen type 3"], ["68", "chevrolet", "chevrolet vega"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["216", "plymouth", "plymouth volare premier v8"], ["338", "renault", "renault lecar deluxe"], ["305", "mercedes", "mercedes benz 300d"], ["46", "chevrolet", "chevrolet impala"], ["295", "mercury", "mercury grand marquis"], ["382", "ford", "ford fairmont futura"], ["96", "ford", "ford gran torino"], ["375", "chrysler", "chrysler lebaron salon"], ["191", "opel", "opel 1900"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["239", "chrysler", "chrysler cordoba"], ["263", "ford", "ford fairmont (man)"], ["160", "plymouth", "plymouth valiant custom"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["283", "volvo", "volvo 264gl"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["169", "chevrolet", "chevrolet chevelle malibu"], ["240", "ford", "ford thunderbird"], ["395", "buick", "buick century limited"], ["347", "buick", "buick skylark"], ["209", "pontiac", "pontiac ventura sj"], ["7", "chevrolet", "chevrolet impala"], ["273", "dodge", "dodge magnum xe"], ["111", "chevrolet", "chevrolet impala"], ["180", "volkswagen", "volkswagen dasher"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["198", "ford", "ford gran torino"], ["372", "buick", "buick century"], ["110", "volkswagen", "volkswagen super beetle"], ["52", "pontiac", "pontiac safari (sw)"], ["317", "volkswagen", "vw rabbit"], ["250", "bmw", "bmw 320i"], ["403", "volkswagen", "vw pickup"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["7", "4", "chevrolet"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["242", "pontiac", "pontiac sunbird coupe"], ["226", "renault", "renault 5 gtl"], ["58", "opel", "opel 1900"], ["286", "volkswagen", "volkswagen scirocco"], ["355", "datsun", "datsun 210 mpg"], ["369", "volvo", "volvo diesel"], ["308", "oldsmobile", "oldsmobile cutlass salon brougham"], ["271", "buick", "buick regal sport coupe (turbo)"], ["266", "buick", "buick century special"], ["101", "plymouth", "plymouth fury gran sedan"], ["401", "chevrolet", "chevrolet camaro"], ["231", "dodge", "dodge monaco brougham"], ["394", "datsun", "datsun 310 gx"], ["151", "opel", "opel manta"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["325", "audi", "audi 4000"], ["40", "volkswagen", "volkswagen super beetle 117"], ["6", "ford", "ford galaxie 500"], ["164", "pontiac", "pontiac catalina"], ["126", "opel", "opel manta"], ["351", "toyota", "toyota starlet"], ["76", "buick", "buick lesabre custom"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Which distinctive models are produced by maker with the full name General Motors or weighing more than 3500?	[["chevrolet"], ["buick"], ["ford"], ["plymouth"], ["pontiac"], ["amc"], ["dodge"], ["mercury"], ["oldsmobile"], ["chrysler"], ["mercedes-benz"], ["cadillac"], ["mercedes"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["221", "chevrolet", "chevy c10"], ["245", "chevrolet", "chevrolet chevette"], ["93", "buick", "buick century 350"], ["173", "chevrolet", "chevrolet monza 2+2"], ["200", "chevrolet", "chevrolet nova"], ["33", "chevrolet", "chevy c20"], ["106", "chevrolet", "chevrolet nova custom"], ["95", "chevrolet", "chevrolet malibu"], ["293", "chevrolet", "chevrolet caprice classic"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["233", "chevrolet", "chevrolet concours"], ["43", "chevrolet", "chevrolet chevelle malibu"], ["229", "chevrolet", "chevrolet caprice classic"], ["301", "volkswagen", "vw rabbit custom"], ["123", "chevrolet", "chevrolet monte carlo s"], ["136", "chevrolet", "chevrolet nova"], ["203", "chevrolet", "chevrolet chevette"], ["299", "chevrolet", "chevrolet malibu classic (sw)"], ["117", "chevrolet", "chevrolet vega"], ["376", "chevrolet", "chevrolet cavalier"], ["349", "chevrolet", "chevrolet citation"], ["161", "chevrolet", "chevrolet nova"], ["124", "pontiac", "pontiac grand prix"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["350", "plymouth", "plymouth reliant"], ["141", "chevrolet", "chevrolet chevelle malibu classic"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["145", "buick", "buick century luxus (sw)"], ["319", "chevrolet", "chevrolet chevette"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["288", "pontiac", "pontiac lemans v6"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["406", "chevrolet", "chevy s-10"], ["19", "chevrolet", "chevrolet monte carlo"], ["165", "chevrolet", "chevrolet bel air"], ["237", "pontiac", "pontiac grand prix lj"], ["241", "volkswagen", "volkswagen rabbit custom"], ["82", "ford", "ford gran torino (sw)"], ["99", "chevrolet", "chevrolet caprice classic"], ["274", "chevrolet", "chevrolet chevette"], ["67", "volkswagen", "volkswagen type 3"], ["334", "volkswagen", "vw dasher (diesel)"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["140", "chevrolet", "chevrolet vega"], ["340", " volkswagen", "volkswagen rabbit"], ["37", "chevrolet", "chevrolet vega 2300"], ["261", "chevrolet", "chevrolet malibu"], ["321", "chevrolet", "chevrolet citation"], ["40", "volkswagen", "volkswagen super beetle 117"], ["63", "volkswagen", "volkswagen model 111"], ["379", "pontiac", "pontiac j2000 se hatchback"], ["73", "ford", "ford galaxie 500"], ["208", "ford", "ford granada ghia"], ["338", "renault", "renault lecar deluxe"], ["46", "chevrolet", "chevrolet impala"], ["178", "pontiac", "pontiac astro"], ["305", "mercedes", "mercedes benz 300d"], ["335", "audi", "audi 5000s (diesel)"], ["314", "chevrolet", "chevrolet citation"], ["110", "volkswagen", "volkswagen super beetle"], ["205", "volkswagen", "vw rabbit"], ["374", "ford", "ford granada gl"], ["103", "buick", "buick electra 225 custom"], ["204", "chevrolet", "chevrolet woody"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["54", "chevrolet", "chevrolet vega (sw)"], ["286", "volkswagen", "volkswagen scirocco"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["225", "buick", "buick opel isuzu deluxe"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["96", "ford", "ford gran torino"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["147", "ford", "ford gran torino (sw)"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["325", "audi", "audi 4000"], ["355", "datsun", "datsun 210 mpg"], ["240", "ford", "ford thunderbird"], ["297", "buick", "buick estate wagon (sw)"], ["7", "chevrolet", "chevrolet impala"], ["283", "volvo", "volvo 264gl"], ["239", "chrysler", "chrysler cordoba"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["180", "volkswagen", "volkswagen dasher"], ["262", "ford", "ford fairmont (auto)"], ["169", "chevrolet", "chevrolet chevelle malibu"], ["68", "chevrolet", "chevrolet vega"], ["111", "chevrolet", "chevrolet impala"], ["87", "renault", "renault 12 (sw)"], ["312", "fiat", "fiat strada custom"], ["356", "toyota", "toyota tercel"], ["144", "ford", "ford gran torino"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["7", "4", "chevrolet"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["242", "pontiac", "pontiac sunbird coupe"], ["209", "pontiac", "pontiac ventura sj"], ["198", "ford", "ford gran torino"], ["164", "pontiac", "pontiac catalina"], ["401", "chevrolet", "chevrolet camaro"], ["317", "volkswagen", "vw rabbit"], ["271", "buick", "buick regal sport coupe (turbo)"], ["250", "bmw", "bmw 320i"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["399", "toyota", "toyota celica gt"], ["394", "datsun", "datsun 310 gx"], ["278", "toyota", "toyota celica gt liftback"], ["382", "ford", "ford fairmont futura"], ["52", "pontiac", "pontiac safari (sw)"], ["226", "renault", "renault 5 gtl"], ["231", "dodge", "dodge monaco brougham"], ["359", "ford", "ford escort 4w"], ["70", "chevrolet", "chevrolet impala"], ["330", "mazda", "mazda glc"], ["308", "oldsmobile", "oldsmobile cutlass salon brougham"], ["285", "peugeot", "peugeot 604sl"], ["122", "fiat", "fiat 124 sport coupe"], ["403", "volkswagen", "vw pickup"], ["347", "buick", "buick skylark"], ["273", "dodge", "dodge magnum xe"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["372", "buick", "buick century"], ["59", "peugeot", "peugeot 304"], ["150", "volkswagen", "volkswagen dasher"], ["101", "plymouth", "plymouth fury gran sedan"], ["386", "mazda", "mazda glc custom"], ["90", "toyota", "toyota corona mark ii (sw)"], ["361", "volkswagen", "volkswagen jetta"], ["92", "toyota", "toyota corolla 1600 (sw)"], ["342", "mazda", "mazda rx-7 gs"], ["351", "toyota", "toyota starlet"], ["384", "volkswagen", "volkswagen rabbit l"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the different models created by either the car maker General Motors or weighed more than 3500?	[["chevrolet"], ["buick"], ["ford"], ["plymouth"], ["pontiac"], ["amc"], ["dodge"], ["mercury"], ["oldsmobile"], ["chrysler"], ["mercedes-benz"], ["cadillac"], ["mercedes"]]	2,048	Answer:
Table cars_data: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["350", "30", "4", "135.0", "84", "2385", "12.9", "1981"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["238", "15.5", "8", "350.0", "170", "4165", "11.4", "1977"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["244", "25.5", "4", "140.0", "89", "2755", "15.8", "1977"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["146", "14", "8", "318.0", "150", "4457", "13.5", "1974"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["154", "28", "4", "90.0", "75", "2125", "14.5", "1974"], ["336", "30", "4", "146.0", "67", "3250", "21.8", "1980"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"], ["164", "16", "8", "400.0", "170", "4668", "11.5", "1975"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["239", "15.5", "8", "400.0", "190", "4325", "12.2", "1977"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["163", "15", "6", "250.0", "72", "3158", "19.5", "1975"], ["77", "12", "8", "350.0", "160", "4456", "13.5", "1972"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["221", "13", "8", "350.0", "145", "4055", "12.0", "1976"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["4", "16", "8", "304.0", "150", "3433", "12.0", "1970"], ["97", "15", "8", "318.0", "150", "3777", "12.5", "1973"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["124", "16", "8", "400.0", "230", "4278", "9.5", "1973"], ["157", "24", "4", "120.0", "97", "2489", "15.0", "1974"], ["35", "9", "8", "304.0", "193", "4732", "18.5", "1970"], ["143", "18", "6", "225.0", "105", "3613", "16.5", "1974"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["200", "22", "6", "250.0", "105", "3353", "14.5", "1976"], ["240", "16", "8", "351.0", "149", "4335", "14.5", "1977"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["111", "11", "8", "400.0", "150", "4997", "14.0", "1973"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["33", "10", "8", "307.0", "200", "4376", "15.0", "1970"], ["373", "26.6", "8", "350.0", "105", "3725", "19.0", "1981"], ["37", "28", "4", "140.0", "90", "2264", "15.5", "1971"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["136", "15", "6", "250.0", "100", "3336", "17.0", "1974"], ["400", "36", "4", "135.0", "84", "2370", "13.0", "1982"], ["153", "32", "4", "83.0", "61", "2003", "19.0", "1974"], ["103", "12", "8", "455.0", "225", "4951", "11.0", "1973"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["180", "25", "4", "90.0", "71", "2223", "16.5", "1975"], ["73", "14", "8", "351.0", "153", "4129", "13.0", "1972"], ["34", "11", "8", "318.0", "210", "4382", "13.5", "1970"], ["92", "27", "4", "97.0", "88", "2100", "16.5", "1972"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["199", "22", "6", "225.0", "100", "3233", "15.4", "1976"], ["12", "null", "8", "350.0", "165", "4142", "11.5", "1970"], ["16", "15", "8", "383.0", "170", "3563", "10.0", "1970"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"], ["88", "22", "4", "122.0", "86", "2395", "16.0", "1972"], ["185", "23", "4", "115.0", "95", "2694", "15.0", "1975"], ["273", "17.5", "8", "318.0", "140", "4080", "13.7", "1978"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:In which years cars were produced weighing no less than 3000 and no more than 4000 ?	[["1970"], ["1971"], ["1972"], ["1973"], ["1974"], ["1975"], ["1976"], ["1977"], ["1978"], ["1979"], ["1980"], ["1981"], ["1982"]]	2,048	Answer:
Table cars_data: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["350", "30", "4", "135.0", "84", "2385", "12.9", "1981"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["238", "15.5", "8", "350.0", "170", "4165", "11.4", "1977"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["244", "25.5", "4", "140.0", "89", "2755", "15.8", "1977"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["154", "28", "4", "90.0", "75", "2125", "14.5", "1974"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["146", "14", "8", "318.0", "150", "4457", "13.5", "1974"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["35", "9", "8", "304.0", "193", "4732", "18.5", "1970"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["143", "18", "6", "225.0", "105", "3613", "16.5", "1974"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"], ["164", "16", "8", "400.0", "170", "4668", "11.5", "1975"], ["77", "12", "8", "350.0", "160", "4456", "13.5", "1972"], ["124", "16", "8", "400.0", "230", "4278", "9.5", "1973"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["163", "15", "6", "250.0", "72", "3158", "19.5", "1975"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["111", "11", "8", "400.0", "150", "4997", "14.0", "1973"], ["239", "15.5", "8", "400.0", "190", "4325", "12.2", "1977"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["49", "14", "8", "318.0", "150", "4096", "13.0", "1971"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["47", "14", "8", "400.0", "175", "4464", "11.5", "1971"], ["37", "28", "4", "140.0", "90", "2264", "15.5", "1971"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["38", "25", "4", "113.0", "95", "2228", "14.0", "1971"], ["4", "16", "8", "304.0", "150", "3433", "12.0", "1970"], ["119", "18", "3", "70.0", "90", "2124", "13.5", "1973"], ["336", "30", "4", "146.0", "67", "3250", "21.8", "1980"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["97", "15", "8", "318.0", "150", "3777", "12.5", "1973"], ["240", "16", "8", "351.0", "149", "4335", "14.5", "1977"], ["400", "36", "4", "135.0", "84", "2370", "13.0", "1982"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["69", "21", "4", "122.0", "86", "2226", "16.5", "1972"], ["200", "22", "6", "250.0", "105", "3353", "14.5", "1976"], ["301", "31.9", "4", "89.0", "71", "1925", "14.0", "1979"], ["73", "14", "8", "351.0", "153", "4129", "13.0", "1972"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["139", "32", "4", "71.0", "65", "1836", "21.0", "1974"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["92", "27", "4", "97.0", "88", "2100", "16.5", "1972"], ["51", "13", "8", "400.0", "170", "4746", "12.0", "1971"], ["33", "10", "8", "307.0", "200", "4376", "15.0", "1970"], ["221", "13", "8", "350.0", "145", "4055", "12.0", "1976"], ["75", "11", "8", "429.0", "208", "4633", "11.0", "1972"], ["88", "22", "4", "122.0", "86", "2395", "16.0", "1972"], ["16", "15", "8", "383.0", "170", "3563", "10.0", "1970"], ["78", "13", "8", "400.0", "190", "4422", "12.5", "1972"], ["58", "28", "4", "116.0", "90", "2123", "14.0", "1971"], ["112", "12", "8", "400.0", "167", "4906", "12.5", "1973"], ["80", "15", "8", "304.0", "150", "3892", "12.5", "1972"], ["1", "18", "8", "307.0", "130", "3504", "12.0", "1970"], ["34", "11", "8", "318.0", "210", "4382", "13.5", "1970"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the different years in which there were cars produced that weighed less than 4000 and also cars that weighted more than 3000 ?	[["1970"], ["1971"], ["1972"], ["1973"], ["1974"], ["1975"], ["1976"], ["1977"], ["1978"], ["1979"], ["1980"], ["1981"], ["1982"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["379", "31", "4", "112.0", "85", "2575", "16.2", "1982"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["385", "37", "4", "91.0", "68", "2025", "18.2", "1982"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["82", "13", "8", "302.0", "140", "4294", "16.0", "1972"], ["297", "16.9", "8", "350.0", "155", "4360", "14.9", "1979"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["389", "36", "4", "120.0", "88", "2160", "14.5", "1982"], ["375", "17.6", "6", "225.0", "85", "3465", "16.6", "1981"], ["396", "38", "6", "262.0", "85", "3015", "17.0", "1982"], ["398", "22", "6", "232.0", "112", "2835", "14.7", "1982"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["399", "32", "4", "144.0", "96", "2665", "13.9", "1982"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["42", "16", "6", "225.0", "105", "3439", "15.5", "1971"], ["406", "31", "4", "119.0", "82", "2720", "19.4", "1982"], ["390", "36", "4", "107.0", "75", "2205", "14.5", "1982"], ["330", "46.6", "4", "86.0", "65", "2110", "17.9", "1980"], ["382", "24", "4", "140.0", "92", "2865", "16.4", "1982"], ["381", "27", "4", "151.0", "90", "2735", "18.0", "1982"], ["402", "27", "4", "140.0", "86", "2790", "15.6", "1982"], ["308", "23.9", "8", "260.0", "90", "3420", "22.2", "1979"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["43", "17", "6", "250.0", "100", "3329", "15.5", "1971"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["395", "25", "6", "181.0", "110", "2945", "16.4", "1982"], ["290", "22.3", "4", "140.0", "88", "2890", "17.3", "1979"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["57", "23", "4", "122.0", "86", "2220", "14.0", "1971"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["47", "14", "8", "400.0", "175", "4464", "11.5", "1971"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["400", "36", "4", "135.0", "84", "2370", "13.0", "1982"], ["53", "18", "6", "258.0", "110", "2962", "13.5", "1971"], ["388", "36", "4", "98.0", "70", "2125", "17.3", "1982"], ["222", "13", "8", "302.0", "130", "3870", "15.0", "1976"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["405", "28", "4", "120.0", "79", "2625", "18.6", "1982"], ["285", "16.2", "6", "163.0", "133", "3410", "15.8", "1978"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["336", "30", "4", "146.0", "67", "3250", "21.8", "1980"], ["350", "30", "4", "135.0", "84", "2385", "12.9", "1981"], ["393", "32", "4", "91.0", "67", "1965", "15.7", "1982"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["220", "16.5", "8", "350.0", "180", "4380", "12.1", "1976"], ["327", "31.3", "4", "120.0", "75", "2542", "17.5", "1980"], ["397", "26", "4", "156.0", "92", "2585", "14.5", "1982"], ["341", "32.7", "6", "168.0", "132", "2910", "11.4", "1980"], ["292", "20.6", "6", "225.0", "110", "3360", "16.6", "1979"], ["334", "43.4", "4", "90.0", "48", "2335", "23.7", "1980"], ["392", "38", "4", "91.0", "67", "1965", "15.0", "1982"], ["136", "15", "6", "250.0", "100", "3336", "17.0", "1974"], ["217", "19", "4", "120.0", "88", "3270", "21.9", "1976"], ["309", "34.2", "4", "105.0", "70", "2200", "13.2", "1979"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["387", "38", "4", "105.0", "63", "2125", "14.7", "1982"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the horsepower of the car with the largest accelerate?	[["71"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["385", "37", "4", "91.0", "68", "2025", "18.2", "1982"], ["350", "30", "4", "135.0", "84", "2385", "12.9", "1981"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["400", "36", "4", "135.0", "84", "2370", "13.0", "1982"], ["82", "13", "8", "302.0", "140", "4294", "16.0", "1972"], ["379", "31", "4", "112.0", "85", "2575", "16.2", "1982"], ["382", "24", "4", "140.0", "92", "2865", "16.4", "1982"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"], ["389", "36", "4", "120.0", "88", "2160", "14.5", "1982"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["390", "36", "4", "107.0", "75", "2205", "14.5", "1982"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["297", "16.9", "8", "350.0", "155", "4360", "14.9", "1979"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["406", "31", "4", "119.0", "82", "2720", "19.4", "1982"], ["402", "27", "4", "140.0", "86", "2790", "15.6", "1982"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["381", "27", "4", "151.0", "90", "2735", "18.0", "1982"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["405", "28", "4", "120.0", "79", "2625", "18.6", "1982"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["398", "22", "6", "232.0", "112", "2835", "14.7", "1982"], ["308", "23.9", "8", "260.0", "90", "3420", "22.2", "1979"], ["396", "38", "6", "262.0", "85", "3015", "17.0", "1982"], ["330", "46.6", "4", "86.0", "65", "2110", "17.9", "1980"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["220", "16.5", "8", "350.0", "180", "4380", "12.1", "1976"], ["222", "13", "8", "302.0", "130", "3870", "15.0", "1976"], ["195", "17.5", "8", "305.0", "140", "4215", "13.0", "1976"], ["399", "32", "4", "144.0", "96", "2665", "13.9", "1982"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["375", "17.6", "6", "225.0", "85", "3465", "16.6", "1981"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["42", "16", "6", "225.0", "105", "3439", "15.5", "1971"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["397", "26", "4", "156.0", "92", "2585", "14.5", "1982"], ["395", "25", "6", "181.0", "110", "2945", "16.4", "1982"], ["335", "36.4", "5", "121.0", "67", "2950", "19.9", "1980"], ["43", "17", "6", "250.0", "100", "3329", "15.5", "1971"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["388", "36", "4", "98.0", "70", "2125", "17.3", "1982"], ["325", "34.3", "4", "97.0", "78", "2188", "15.8", "1980"], ["354", "32.3", "4", "97.0", "67", "2065", "17.8", "1981"], ["387", "38", "4", "105.0", "63", "2125", "14.7", "1982"], ["57", "23", "4", "122.0", "86", "2220", "14.0", "1971"], ["229", "17.5", "8", "305.0", "145", "3880", "12.5", "1977"], ["340", "29.8", "4", "89.0", "62", "1845", "15.3", "1980"], ["89", "28", "4", "97.0", "92", "2288", "17.0", "1972"], ["225", "30", "4", "111.0", "80", "2155", "14.8", "1977"], ["180", "25", "4", "90.0", "71", "2223", "16.5", "1975"], ["221", "13", "8", "350.0", "145", "4055", "12.0", "1976"], ["326", "29.8", "4", "134.0", "90", "2711", "15.5", "1980"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["217", "19", "4", "120.0", "88", "3270", "21.9", "1976"], ["290", "22.3", "4", "140.0", "88", "2890", "17.3", "1979"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the horsepower of the car with the greatest accelerate?	[["71"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["84", "volvo", "volvo 145e (sw)"], ["283", "volvo", "volvo 264gl"], ["128", "volvo", "volvo 144ea"], ["215", "volvo", "volvo 245"], ["369", "volvo", "volvo diesel"], ["187", "volvo", "volvo 244dl"], ["194", "renault", "renault 12tl"], ["59", "peugeot", "peugeot 304"], ["87", "renault", "renault 12 (sw)"], ["285", "peugeot", "peugeot 604sl"], ["307", "peugeot", "peugeot 504"], ["340", " volkswagen", "volkswagen rabbit"], ["334", "volkswagen", "vw dasher (diesel)"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["338", "renault", "renault lecar deluxe"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["335", "audi", "audi 5000s (diesel)"], ["27", "peugeot", "peugeot 504"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["384", "volkswagen", "volkswagen rabbit l"], ["286", "volkswagen", "volkswagen scirocco"], ["362", "renault", "renault 18i"], ["150", "volkswagen", "volkswagen dasher"], ["241", "volkswagen", "volkswagen rabbit custom"], ["186", "peugeot", "peugeot 504"], ["180", "volkswagen", "volkswagen dasher"], ["86", "peugeot", "peugeot 504 (sw)"], ["217", "peugeot", "peugeot 504"], ["40", "volkswagen", "volkswagen super beetle 117"], ["183", "volkswagen", "volkswagen rabbit"], ["185", "audi", "audi 100ls"], ["211", "volkswagen", "volkswagen rabbit"], ["248", "volkswagen", "volkswagen dasher"], ["50", "dodge", "dodge monaco (sw)"], ["122", "fiat", "fiat 124 sport coupe"], ["67", "volkswagen", "volkswagen type 3"], ["317", "volkswagen", "vw rabbit"], ["152", "toyota", "toyota corona"], ["149", "audi", "audi fox"], ["231", "dodge", "dodge monaco brougham"], ["356", "toyota", "toyota tercel"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["110", "volkswagen", "volkswagen super beetle"], ["216", "plymouth", "plymouth volare premier v8"], ["69", "ford", "ford pinto runabout"], ["127", "audi", "audi 100ls"], ["361", "volkswagen", "volkswagen jetta"], ["126", "opel", "opel manta"], ["190", "fiat", "fiat 131"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["196", "dodge", "dodge coronet brougham"], ["205", "volkswagen", "vw rabbit"], ["226", "renault", "renault 5 gtl"], ["63", "volkswagen", "volkswagen model 111"], ["34", "dodge", "dodge d200"], ["325", "audi", "audi 4000"], ["239", "chrysler", "chrysler cordoba"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["193", "dodge", "dodge colt"], ["277", "dodge", "dodge omni"], ["89", "datsun", "datsun 510 (sw)"], ["2", "buick", "buick skylark 320"], ["131", "toyota", "toyota mark ii"], ["158", "subaru", "subaru"], ["351", "toyota", "toyota starlet"], ["13", "ford", "ford torino (sw)"], ["38", "toyota", "toyota corona"], ["116", "toyota", "toyota carina"], ["370", "toyota", "toyota cressida"], ["397", "chrysler", "chrysler lebaron medallion"], ["229", "chevrolet", "chevrolet caprice classic"], ["179", "toyota", "toyota corona"], ["134", "ford", "ford maverick"], ["120", "ford", "ford pinto"], ["360", "ford", "ford escort 2h"], ["28", "audi", "audi 100 ls"], ["380", "dodge", "dodge aries se"], ["62", "datsun", "datsun 1200"], ["90", "toyota", "toyota corona mark ii (sw)"], ["125", "fiat", "fiat 128"], ["78", "chrysler", "chrysler newport royal"], ["403", "volkswagen", "vw pickup"], ["288", "pontiac", "pontiac lemans v6"], ["191", "opel", "opel 1900"], ["348", "dodge", "dodge aries wagon (sw)"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["355", "datsun", "datsun 210 mpg"], ["354", "subaru", "subaru"], ["58", "opel", "opel 1900"], ["282", "audi", "audi 5000"], ["147", "ford", "ford gran torino (sw)"], ["60", "fiat", "fiat 124b"], ["156", "fiat", "fiat 124 tc"], ["376", "chevrolet", "chevrolet cavalier"], ["343", "triumph", "triumph tr7 coupe"], ["39", "ford", "ford pinto"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["20", "buick", "buick estate wagon (sw)"], ["155", "fiat", "fiat 128"], ["176", "ford", "ford pinto"], ["301", "volkswagen", "vw rabbit custom"], ["146", "dodge", "dodge coronet custom (sw)"], ["218", "toyota", "toyota mark ii"], ["359", "ford", "ford escort 4w"], ["151", "opel", "opel manta"], ["326", "toyota", "toyota corona liftback"], ["240", "ford", "ford thunderbird"], ["405", "ford", "ford ranger"], ["250", "bmw", "bmw 320i"], ["32", "ford", "ford f250"], ["21", "toyota", "toyota corona mark ii"], ["16", "dodge", "dodge challenger se"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["145", "buick", "buick century luxus (sw)"], ["173", "chevrolet", "chevrolet monza 2+2"], ["275", "toyota", "toyota corona"], ["138", "ford", "ford pinto"], ["344", "ford", "ford mustang cobra"], ["82", "ford", "ford gran torino (sw)"], ["88", "ford", "ford pinto (sw)"], ["123", "chevrolet", "chevrolet monte carlo s"], ["273", "dodge", "dodge magnum xe"], ["262", "ford", "ford fairmont (auto)"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["48", "ford", "ford galaxie 500"], ["247", "subaru", "subaru dl"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["245", "chevrolet", "chevrolet chevette"], ["363", "honda", "honda prelude"], ["223", "dodge", "dodge d100"], ["72", "plymouth", "plymouth fury iii"], ["224", "honda", "honda accord cvcc"], ["339", "subaru", "subaru dl"], ["234", "buick", "buick skylark"], ["44", "ford", "ford torino 500"], ["276", "datsun", "datsun 510"], ["220", "cadillac", "cadillac seville"], ["65", "toyota", "toyota corona hardtop"], ["399", "toyota", "toyota celica gt"], ["198", "ford", "ford gran torino"], ["306", "cadillac", "cadillac eldorado"], ["24", "ford", "ford maverick"], ["214", "ford", "ford pinto"], ["382", "ford", "ford fairmont futura"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:For model volvo, how many cylinders does the car with the least accelerate have?	[["6"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["84", "volvo", "volvo 145e (sw)"], ["283", "volvo", "volvo 264gl"], ["128", "volvo", "volvo 144ea"], ["187", "volvo", "volvo 244dl"], ["215", "volvo", "volvo 245"], ["369", "volvo", "volvo diesel"], ["194", "renault", "renault 12tl"], ["87", "renault", "renault 12 (sw)"], ["285", "peugeot", "peugeot 604sl"], ["59", "peugeot", "peugeot 304"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["340", " volkswagen", "volkswagen rabbit"], ["307", "peugeot", "peugeot 504"], ["216", "plymouth", "plymouth volare premier v8"], ["362", "renault", "renault 18i"], ["334", "volkswagen", "vw dasher (diesel)"], ["335", "audi", "audi 5000s (diesel)"], ["338", "renault", "renault lecar deluxe"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["241", "volkswagen", "volkswagen rabbit custom"], ["185", "audi", "audi 100ls"], ["384", "volkswagen", "volkswagen rabbit l"], ["150", "volkswagen", "volkswagen dasher"], ["27", "peugeot", "peugeot 504"], ["217", "peugeot", "peugeot 504"], ["86", "peugeot", "peugeot 504 (sw)"], ["286", "volkswagen", "volkswagen scirocco"], ["211", "volkswagen", "volkswagen rabbit"], ["183", "volkswagen", "volkswagen rabbit"], ["180", "volkswagen", "volkswagen dasher"], ["40", "volkswagen", "volkswagen super beetle 117"], ["186", "peugeot", "peugeot 504"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["67", "volkswagen", "volkswagen type 3"], ["50", "dodge", "dodge monaco (sw)"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["317", "volkswagen", "vw rabbit"], ["231", "dodge", "dodge monaco brougham"], ["248", "volkswagen", "volkswagen dasher"], ["343", "triumph", "triumph tr7 coupe"], ["191", "opel", "opel 1900"], ["126", "opel", "opel manta"], ["361", "volkswagen", "volkswagen jetta"], ["127", "audi", "audi 100ls"], ["69", "ford", "ford pinto runabout"], ["149", "audi", "audi fox"], ["196", "dodge", "dodge coronet brougham"], ["190", "fiat", "fiat 131"], ["226", "renault", "renault 5 gtl"], ["380", "dodge", "dodge aries se"], ["89", "datsun", "datsun 510 (sw)"], ["122", "fiat", "fiat 124 sport coupe"], ["2", "buick", "buick skylark 320"], ["356", "toyota", "toyota tercel"], ["152", "toyota", "toyota corona"], ["205", "volkswagen", "vw rabbit"], ["72", "plymouth", "plymouth fury iii"], ["34", "dodge", "dodge d200"], ["63", "volkswagen", "volkswagen model 111"], ["360", "ford", "ford escort 2h"], ["58", "opel", "opel 1900"], ["110", "volkswagen", "volkswagen super beetle"], ["78", "chrysler", "chrysler newport royal"], ["325", "audi", "audi 4000"], ["229", "chevrolet", "chevrolet caprice classic"], ["288", "pontiac", "pontiac lemans v6"], ["348", "dodge", "dodge aries wagon (sw)"], ["193", "dodge", "dodge colt"], ["131", "toyota", "toyota mark ii"], ["62", "datsun", "datsun 1200"], ["158", "subaru", "subaru"], ["397", "chrysler", "chrysler lebaron medallion"], ["355", "datsun", "datsun 210 mpg"], ["146", "dodge", "dodge coronet custom (sw)"], ["239", "chrysler", "chrysler cordoba"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["240", "ford", "ford thunderbird"], ["301", "volkswagen", "vw rabbit custom"], ["244", "ford", "ford mustang ii 2+2"], ["121", "mercury", "mercury capri v6"], ["247", "subaru", "subaru dl"], ["359", "ford", "ford escort 4w"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["277", "dodge", "dodge omni"], ["32", "ford", "ford f250"], ["151", "opel", "opel manta"], ["354", "subaru", "subaru"], ["147", "ford", "ford gran torino (sw)"], ["28", "audi", "audi 100 ls"], ["339", "subaru", "subaru dl"], ["156", "fiat", "fiat 124 tc"], ["350", "plymouth", "plymouth reliant"], ["403", "volkswagen", "vw pickup"], ["13", "ford", "ford torino (sw)"], ["20", "buick", "buick estate wagon (sw)"], ["223", "dodge", "dodge d100"], ["16", "dodge", "dodge challenger se"], ["155", "fiat", "fiat 128"], ["145", "buick", "buick century luxus (sw)"], ["8", "plymouth", "plymouth fury iii"], ["262", "ford", "ford fairmont (auto)"], ["225", "buick", "buick opel isuzu deluxe"], ["134", "ford", "ford maverick"], ["218", "toyota", "toyota mark ii"], ["370", "toyota", "toyota cressida"], ["344", "ford", "ford mustang cobra"], ["351", "toyota", "toyota starlet"], ["363", "honda", "honda prelude"], ["382", "ford", "ford fairmont futura"], ["282", "audi", "audi 5000"], ["310", "plymouth", "plymouth horizon tc3"], ["125", "fiat", "fiat 128"], ["90", "toyota", "toyota corona mark ii (sw)"], ["250", "bmw", "bmw 320i"], ["294", "ford", "ford ltd landau"], ["176", "ford", "ford pinto"], ["120", "ford", "ford pinto"], ["366", "mazda", "mazda 626"], ["328", "datsun", "datsun 510 hatchback"], ["276", "datsun", "datsun 510"], ["376", "chevrolet", "chevrolet cavalier"], ["273", "dodge", "dodge magnum xe"], ["60", "fiat", "fiat 124b"], ["179", "toyota", "toyota corona"], ["290", "ford", "ford fairmont 4"], ["405", "ford", "ford ranger"], ["39", "ford", "ford pinto"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["320", "datsun", "datsun 310"], ["298", "ford", "ford country squire (sw)"], ["82", "ford", "ford gran torino (sw)"], ["224", "honda", "honda accord cvcc"], ["326", "toyota", "toyota corona liftback"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["173", "chevrolet", "chevrolet monza 2+2"], ["48", "ford", "ford galaxie 500"], ["347", "buick", "buick skylark"], ["21", "toyota", "toyota corona mark ii"], ["160", "plymouth", "plymouth valiant custom"], ["36", "datsun", "datsun pl510"], ["371", "datsun", "datsun 810 maxima"], ["101", "plymouth", "plymouth fury gran sedan"], ["234", "buick", "buick skylark"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:For a volvo model, how many cylinders does the version with least accelerate have?	[["6"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["398", "22", "6", "232.0", "112", "2835", "14.7", "1982"], ["399", "32", "4", "144.0", "96", "2665", "13.9", "1982"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["389", "36", "4", "120.0", "88", "2160", "14.5", "1982"], ["396", "38", "6", "262.0", "85", "3015", "17.0", "1982"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["379", "31", "4", "112.0", "85", "2575", "16.2", "1982"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["82", "13", "8", "302.0", "140", "4294", "16.0", "1972"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["385", "37", "4", "91.0", "68", "2025", "18.2", "1982"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["397", "26", "4", "156.0", "92", "2585", "14.5", "1982"], ["297", "16.9", "8", "350.0", "155", "4360", "14.9", "1979"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["42", "16", "6", "225.0", "105", "3439", "15.5", "1971"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["382", "24", "4", "140.0", "92", "2865", "16.4", "1982"], ["57", "23", "4", "122.0", "86", "2220", "14.0", "1971"], ["390", "36", "4", "107.0", "75", "2205", "14.5", "1982"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["402", "27", "4", "140.0", "86", "2790", "15.6", "1982"], ["393", "32", "4", "91.0", "67", "1965", "15.7", "1982"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["43", "17", "6", "250.0", "100", "3329", "15.5", "1971"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["375", "17.6", "6", "225.0", "85", "3465", "16.6", "1981"], ["341", "32.7", "6", "168.0", "132", "2910", "11.4", "1980"], ["136", "15", "6", "250.0", "100", "3336", "17.0", "1974"], ["381", "27", "4", "151.0", "90", "2735", "18.0", "1982"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["392", "38", "4", "91.0", "67", "1965", "15.0", "1982"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"], ["308", "23.9", "8", "260.0", "90", "3420", "22.2", "1979"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["157", "24", "4", "120.0", "97", "2489", "15.0", "1974"], ["387", "38", "4", "105.0", "63", "2125", "14.7", "1982"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["53", "18", "6", "258.0", "110", "2962", "13.5", "1971"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["103", "12", "8", "455.0", "225", "4951", "11.0", "1973"], ["395", "25", "6", "181.0", "110", "2945", "16.4", "1982"], ["334", "43.4", "4", "90.0", "48", "2335", "23.7", "1980"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["34", "11", "8", "318.0", "210", "4382", "13.5", "1970"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["388", "36", "4", "98.0", "70", "2125", "17.3", "1982"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["47", "14", "8", "400.0", "175", "4464", "11.5", "1971"], ["386", "31", "4", "91.0", "68", "1970", "17.6", "1982"], ["330", "46.6", "4", "86.0", "65", "2110", "17.9", "1980"], ["222", "13", "8", "302.0", "130", "3870", "15.0", "1976"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"], ["336", "30", "4", "146.0", "67", "3250", "21.8", "1980"], ["333", "44.3", "4", "90.0", "48", "2085", "21.7", "1980"], ["121", "21", "6", "155.0", "107", "2472", "14.0", "1973"], ["153", "32", "4", "83.0", "61", "2003", "19.0", "1974"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:How many cars have a larger accelerate than the car with the largest horsepower?	[["39"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["398", "22", "6", "232.0", "112", "2835", "14.7", "1982"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["385", "37", "4", "91.0", "68", "2025", "18.2", "1982"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["379", "31", "4", "112.0", "85", "2575", "16.2", "1982"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["389", "36", "4", "120.0", "88", "2160", "14.5", "1982"], ["396", "38", "6", "262.0", "85", "3015", "17.0", "1982"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["402", "27", "4", "140.0", "86", "2790", "15.6", "1982"], ["381", "27", "4", "151.0", "90", "2735", "18.0", "1982"], ["399", "32", "4", "144.0", "96", "2665", "13.9", "1982"], ["82", "13", "8", "302.0", "140", "4294", "16.0", "1972"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"], ["43", "17", "6", "250.0", "100", "3329", "15.5", "1971"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["382", "24", "4", "140.0", "92", "2865", "16.4", "1982"], ["297", "16.9", "8", "350.0", "155", "4360", "14.9", "1979"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["390", "36", "4", "107.0", "75", "2205", "14.5", "1982"], ["136", "15", "6", "250.0", "100", "3336", "17.0", "1974"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["103", "12", "8", "455.0", "225", "4951", "11.0", "1973"], ["42", "16", "6", "225.0", "105", "3439", "15.5", "1971"], ["397", "26", "4", "156.0", "92", "2585", "14.5", "1982"], ["405", "28", "4", "120.0", "79", "2625", "18.6", "1982"], ["195", "17.5", "8", "305.0", "140", "4215", "13.0", "1976"], ["395", "25", "6", "181.0", "110", "2945", "16.4", "1982"], ["115", "18", "6", "232.0", "100", "2789", "15.0", "1973"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["308", "23.9", "8", "260.0", "90", "3420", "22.2", "1979"], ["217", "19", "4", "120.0", "88", "3270", "21.9", "1976"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["2", "15", "8", "350.0", "165", "3693", "11.5", "1970"], ["57", "23", "4", "122.0", "86", "2220", "14.0", "1971"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["393", "32", "4", "91.0", "67", "1965", "15.7", "1982"], ["341", "32.7", "6", "168.0", "132", "2910", "11.4", "1980"], ["406", "31", "4", "119.0", "82", "2720", "19.4", "1982"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["135", "19", "6", "232.0", "100", "2901", "16.0", "1974"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["392", "38", "4", "91.0", "67", "1965", "15.0", "1982"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["375", "17.6", "6", "225.0", "85", "3465", "16.6", "1981"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["350", "30", "4", "135.0", "84", "2385", "12.9", "1981"], ["400", "36", "4", "135.0", "84", "2370", "13.0", "1982"], ["386", "31", "4", "91.0", "68", "1970", "17.6", "1982"], ["54", "22", "4", "140.0", "72", "2408", "19.0", "1971"], ["155", "24", "4", "90.0", "75", "2108", "15.5", "1974"], ["100", "13", "8", "351.0", "158", "4363", "13.0", "1973"], ["299", "19.2", "8", "267.0", "125", "3605", "15.0", "1979"], ["326", "29.8", "4", "134.0", "90", "2711", "15.5", "1980"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["290", "22.3", "4", "140.0", "88", "2890", "17.3", "1979"], ["330", "46.6", "4", "86.0", "65", "2110", "17.9", "1980"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the number of cars with a greater accelerate than the one with the most horsepower?	[["39"]]	2,048	Answer:
Table car_makers: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"], ["2", "volkswagen", "Volkswagen", "2"], ["8", "nissan", "Nissan Motors", "4"]]Table countries: [["CountryId", "CountryName", "Continent"], ["2", "germany", "2"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"], ["16", "renault", "Renault", "3"], ["22", "kia", "Kia Motors", "8"]]Table countries: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"], ["3", "france", "2"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["3", "bmw", "BMW", "2"], ["1", "amc", "American Motor Company", "1"], ["14", "opel", "Opel", "2"], ["7", "citroen", "Citroen", "3"], ["5", "ford", "Ford Motor Company", "1"], ["21", "volvo", "Volvo", "6"]]Table countries: [["CountryId", "CountryName", "Continent"], ["6", "sweden", "2"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["19", "toyota", "Toyota", "4"], ["6", "chrysler", "Chrysler", "1"], ["23", "hyundai", "Hyundai", "8"]]Table countries: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"], ["7", "uk", "2"], ["5", "italy", "2"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["11", "honda", "Honda", "4"], ["9", "fiat", "Fiat", "5"], ["18", "subaru", "Subaru", "4"]]Table countries: [["CountryId", "CountryName", "Continent"], ["4", "japan", "3"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"]]Table countries: [["CountryId", "CountryName", "Continent"], ["8", "korea", "3"], ["14", "mexico", "1"], ["10", "nigeria", "4"], ["12", "new zealand", "5"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table countries: [["CountryId", "CountryName", "Continent"], ["11", "australia", "5"], ["1", "usa", "1"], ["13", "egypt", "4"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"], ["20", "triumph", "Triumph", "7"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:How many countries has more than 2 car makers ?	[["4"], ["4"], ["3"], ["5"]]	2,048	Answer:
Table car_makers: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"], ["8", "nissan", "Nissan Motors", "4"], ["2", "volkswagen", "Volkswagen", "2"]]Table countries: [["CountryId", "CountryName", "Continent"], ["2", "germany", "2"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["16", "renault", "Renault", "3"], ["22", "kia", "Kia Motors", "8"], ["4", "gm", "General Motors", "1"]]Table countries: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"], ["3", "france", "2"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["3", "bmw", "BMW", "2"], ["21", "volvo", "Volvo", "6"], ["14", "opel", "Opel", "2"], ["7", "citroen", "Citroen", "3"], ["19", "toyota", "Toyota", "4"], ["5", "ford", "Ford Motor Company", "1"], ["23", "hyundai", "Hyundai", "8"], ["6", "chrysler", "Chrysler", "1"]]Table countries: [["CountryId", "CountryName", "Continent"], ["6", "sweden", "2"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"]]Table countries: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["11", "honda", "Honda", "4"]]Table countries: [["CountryId", "CountryName", "Continent"], ["7", "uk", "2"], ["5", "italy", "2"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"], ["18", "subaru", "Subaru", "4"]]Table countries: [["CountryId", "CountryName", "Continent"], ["4", "japan", "3"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"]]Table countries: [["CountryId", "CountryName", "Continent"], ["8", "korea", "3"], ["14", "mexico", "1"], ["12", "new zealand", "5"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table countries: [["CountryId", "CountryName", "Continent"], ["10", "nigeria", "4"], ["11", "australia", "5"], ["13", "egypt", "4"], ["1", "usa", "1"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"], ["20", "triumph", "Triumph", "7"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the number of countries with more than 2 car makers ?	[["4"], ["4"], ["3"], ["5"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["136", "15", "6", "250.0", "100", "3336", "17.0", "1974"], ["396", "38", "6", "262.0", "85", "3015", "17.0", "1982"], ["268", "18.6", "6", "225.0", "110", "3620", "18.7", "1978"], ["121", "21", "6", "155.0", "107", "2472", "14.0", "1973"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["108", "18", "6", "250.0", "88", "3021", "16.5", "1973"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["201", "24", "6", "200.0", "81", "3012", "17.6", "1976"], ["283", "17", "6", "163.0", "125", "3140", "13.6", "1978"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["106", "16", "6", "250.0", "100", "3278", "18.0", "1973"], ["200", "22", "6", "250.0", "105", "3353", "14.5", "1976"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["43", "17", "6", "250.0", "100", "3329", "15.5", "1971"], ["146", "14", "8", "318.0", "150", "4457", "13.5", "1974"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["112", "12", "8", "400.0", "167", "4906", "12.5", "1973"], ["398", "22", "6", "232.0", "112", "2835", "14.7", "1982"], ["236", "18.5", "6", "250.0", "98", "3525", "19.0", "1977"], ["6", "15", "8", "429.0", "198", "4341", "10.0", "1970"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["164", "16", "8", "400.0", "170", "4668", "11.5", "1975"], ["163", "15", "6", "250.0", "72", "3158", "19.5", "1975"], ["135", "19", "6", "232.0", "100", "2901", "16.0", "1974"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["375", "17.6", "6", "225.0", "85", "3465", "16.6", "1981"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["179", "24", "4", "134.0", "96", "2702", "13.5", "1975"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["52", "13", "8", "400.0", "175", "5140", "12.0", "1971"], ["292", "20.6", "6", "225.0", "110", "3360", "16.6", "1979"], ["77", "12", "8", "350.0", "160", "4456", "13.5", "1972"], ["186", "23", "4", "120.0", "88", "2957", "17.0", "1975"], ["273", "17.5", "8", "318.0", "140", "4080", "13.7", "1978"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["126", "24", "4", "116.0", "75", "2158", "15.5", "1973"], ["370", "25.4", "6", "168.0", "116", "2900", "12.6", "1981"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["192", "25", "4", "140.0", "92", "2572", "14.9", "1976"], ["78", "13", "8", "400.0", "190", "4422", "12.5", "1972"], ["221", "13", "8", "350.0", "145", "4055", "12.0", "1976"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["169", "16", "6", "250.0", "105", "3897", "18.5", "1975"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["100", "13", "8", "351.0", "158", "4363", "13.0", "1973"], ["195", "17.5", "8", "305.0", "140", "4215", "13.0", "1976"], ["266", "20.6", "6", "231.0", "105", "3380", "15.8", "1978"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["49", "14", "8", "318.0", "150", "4096", "13.0", "1971"], ["88", "22", "4", "122.0", "86", "2395", "16.0", "1972"], ["369", "30.7", "6", "145.0", "76", "3160", "19.6", "1981"], ["172", "21", "6", "231.0", "110", "3039", "15.0", "1975"], ["41", "19", "6", "232.0", "100", "2634", "13.0", "1971"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:How many cars has over 6 cylinders?	[["108"]]	2,048	Answer:
Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["396", "38", "6", "262.0", "85", "3015", "17.0", "1982"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["108", "18", "6", "250.0", "88", "3021", "16.5", "1973"], ["76", "13", "8", "350.0", "155", "4502", "13.5", "1972"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["136", "15", "6", "250.0", "100", "3336", "17.0", "1974"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["283", "17", "6", "163.0", "125", "3140", "13.6", "1978"], ["163", "15", "6", "250.0", "72", "3158", "19.5", "1975"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["200", "22", "6", "250.0", "105", "3353", "14.5", "1976"], ["268", "18.6", "6", "225.0", "110", "3620", "18.7", "1978"], ["201", "24", "6", "200.0", "81", "3012", "17.6", "1976"], ["43", "17", "6", "250.0", "100", "3329", "15.5", "1971"], ["121", "21", "6", "155.0", "107", "2472", "14.0", "1973"], ["50", "12", "8", "383.0", "180", "4955", "11.5", "1971"], ["179", "24", "4", "134.0", "96", "2702", "13.5", "1975"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["375", "17.6", "6", "225.0", "85", "3465", "16.6", "1981"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["236", "18.5", "6", "250.0", "98", "3525", "19.0", "1977"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["52", "13", "8", "400.0", "175", "5140", "12.0", "1971"], ["42", "16", "6", "225.0", "105", "3439", "15.5", "1971"], ["186", "23", "4", "120.0", "88", "2957", "17.0", "1975"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["369", "30.7", "6", "145.0", "76", "3160", "19.6", "1981"], ["266", "20.6", "6", "231.0", "105", "3380", "15.8", "1978"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["6", "15", "8", "429.0", "198", "4341", "10.0", "1970"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["112", "12", "8", "400.0", "167", "4906", "12.5", "1973"], ["106", "16", "6", "250.0", "100", "3278", "18.0", "1973"], ["185", "23", "4", "115.0", "95", "2694", "15.0", "1975"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["77", "12", "8", "350.0", "160", "4456", "13.5", "1972"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["292", "20.6", "6", "225.0", "110", "3360", "16.6", "1979"], ["41", "19", "6", "232.0", "100", "2634", "13.0", "1971"], ["164", "16", "8", "400.0", "170", "4668", "11.5", "1975"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["398", "22", "6", "232.0", "112", "2835", "14.7", "1982"], ["374", "20.2", "6", "200.0", "88", "3060", "17.1", "1981"], ["78", "13", "8", "400.0", "190", "4422", "12.5", "1972"], ["97", "15", "8", "318.0", "150", "3777", "12.5", "1973"], ["169", "16", "6", "250.0", "105", "3897", "18.5", "1975"], ["49", "14", "8", "318.0", "150", "4096", "13.0", "1971"], ["53", "18", "6", "258.0", "110", "2962", "13.5", "1971"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["293", "17", "8", "305.0", "130", "3840", "15.4", "1979"], ["126", "24", "4", "116.0", "75", "2158", "15.5", "1973"], ["177", "20", "6", "232.0", "100", "2914", "16.0", "1975"], ["135", "19", "6", "232.0", "100", "2901", "16.0", "1974"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["192", "25", "4", "140.0", "92", "2572", "14.9", "1976"], ["115", "18", "6", "232.0", "100", "2789", "15.0", "1973"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["146", "14", "8", "318.0", "150", "4457", "13.5", "1974"], ["370", "25.4", "6", "168.0", "116", "2900", "12.6", "1981"], ["124", "16", "8", "400.0", "230", "4278", "9.5", "1973"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the number of carsw ith over 6 cylinders?	[["108"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["217", "peugeot", "peugeot 504"], ["285", "peugeot", "peugeot 604sl"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["59", "peugeot", "peugeot 304"], ["216", "plymouth", "plymouth volare premier v8"], ["186", "peugeot", "peugeot 504"], ["384", "volkswagen", "volkswagen rabbit l"], ["27", "peugeot", "peugeot 504"], ["86", "peugeot", "peugeot 504 (sw)"], ["338", "renault", "renault lecar deluxe"], ["87", "renault", "renault 12 (sw)"], ["229", "chevrolet", "chevrolet caprice classic"], ["344", "ford", "ford mustang cobra"], ["293", "chevrolet", "chevrolet caprice classic"], ["194", "renault", "renault 12tl"], ["96", "ford", "ford gran torino"], ["334", "volkswagen", "vw dasher (diesel)"], ["340", " volkswagen", "volkswagen rabbit"], ["82", "ford", "ford gran torino (sw)"], ["307", "peugeot", "peugeot 504"], ["147", "ford", "ford gran torino (sw)"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["226", "renault", "renault 5 gtl"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["356", "toyota", "toyota tercel"], ["5", "ford", "ford torino"], ["359", "ford", "ford escort 4w"], ["374", "ford", "ford granada gl"], ["99", "chevrolet", "chevrolet caprice classic"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["144", "ford", "ford gran torino"], ["317", "volkswagen", "vw rabbit"], ["44", "ford", "ford torino 500"], ["46", "chevrolet", "chevrolet impala"], ["245", "chevrolet", "chevrolet chevette"], ["205", "volkswagen", "vw rabbit"], ["241", "volkswagen", "volkswagen rabbit custom"], ["376", "chevrolet", "chevrolet cavalier"], ["18", "ford", "ford mustang boss 302"], ["198", "ford", "ford gran torino"], ["78", "chrysler", "chrysler newport royal"], ["362", "renault", "renault 18i"], ["84", "volvo", "volvo 145e (sw)"], ["126", "opel", "opel manta"], ["319", "chevrolet", "chevrolet chevette"], ["211", "volkswagen", "volkswagen rabbit"], ["7", "chevrolet", "chevrolet impala"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["183", "volkswagen", "volkswagen rabbit"], ["239", "chrysler", "chrysler cordoba"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["335", "audi", "audi 5000s (diesel)"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["288", "pontiac", "pontiac lemans v6"], ["123", "chevrolet", "chevrolet monte carlo s"], ["355", "datsun", "datsun 210 mpg"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["180", "volkswagen", "volkswagen dasher"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["278", "toyota", "toyota celica gt liftback"], ["54", "chevrolet", "chevrolet vega (sw)"], ["110", "volkswagen", "volkswagen super beetle"], ["173", "chevrolet", "chevrolet monza 2+2"], ["161", "chevrolet", "chevrolet nova"], ["371", "datsun", "datsun 810 maxima"], ["286", "volkswagen", "volkswagen scirocco"], ["69", "ford", "ford pinto runabout"], ["290", "ford", "ford fairmont 4"], ["328", "datsun", "datsun 510 hatchback"], ["369", "volvo", "volvo diesel"], ["165", "chevrolet", "chevrolet bel air"], ["200", "chevrolet", "chevrolet nova"], ["13", "ford", "ford torino (sw)"], ["70", "chevrolet", "chevrolet impala"], ["40", "volkswagen", "volkswagen super beetle 117"], ["273", "dodge", "dodge magnum xe"], ["262", "ford", "ford fairmont (auto)"], ["11", "citroen", "citroen ds-21 pallas"], ["343", "triumph", "triumph tr7 coupe"], ["399", "toyota", "toyota celica gt"], ["152", "toyota", "toyota corona"], ["305", "mercedes", "mercedes benz 300d"], ["203", "chevrolet", "chevrolet chevette"], ["364", "toyota", "toyota corolla"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["250", "bmw", "bmw 320i"], ["326", "toyota", "toyota corona liftback"], ["283", "volvo", "volvo 264gl"], ["101", "plymouth", "plymouth fury gran sedan"], ["351", "toyota", "toyota starlet"], ["233", "chevrolet", "chevrolet concours"], ["236", "ford", "ford granada"], ["174", "ford", "ford mustang ii"], ["318", "toyota", "toyota corolla tercel"], ["140", "chevrolet", "chevrolet vega"], ["111", "chevrolet", "chevrolet impala"], ["390", "honda", "honda accord"], ["370", "toyota", "toyota cressida"], ["361", "volkswagen", "volkswagen jetta"], ["191", "opel", "opel 1900"], ["208", "ford", "ford granada ghia"], ["150", "volkswagen", "volkswagen dasher"], ["116", "toyota", "toyota carina"], ["145", "buick", "buick century luxus (sw)"], ["106", "chevrolet", "chevrolet nova custom"], ["394", "datsun", "datsun 310 gx"], ["301", "volkswagen", "vw rabbit custom"], ["325", "audi", "audi 4000"], ["375", "chrysler", "chrysler lebaron salon"], ["63", "volkswagen", "volkswagen model 111"], ["248", "volkswagen", "volkswagen dasher"], ["73", "ford", "ford galaxie 500"], ["128", "volvo", "volvo 144ea"], ["151", "opel", "opel manta"], ["357", "mazda", "mazda glc 4"], ["240", "ford", "ford thunderbird"], ["176", "ford", "ford pinto"], ["67", "volkswagen", "volkswagen type 3"], ["68", "chevrolet", "chevrolet vega"], ["38", "toyota", "toyota corona"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["156", "fiat", "fiat 124 tc"], ["43", "chevrolet", "chevrolet chevelle malibu"], ["274", "chevrolet", "chevrolet chevette"], ["179", "toyota", "toyota corona"], ["215", "volvo", "volvo 245"], ["306", "cadillac", "cadillac eldorado"], ["244", "ford", "ford mustang ii 2+2"], ["204", "chevrolet", "chevrolet woody"], ["48", "ford", "ford galaxie 500"], ["58", "opel", "opel 1900"], ["182", "ford", "ford pinto"], ["39", "ford", "ford pinto"], ["350", "plymouth", "plymouth reliant"], ["136", "chevrolet", "chevrolet nova"], ["337", "honda", "honda civic 1500 gl"], ["19", "chevrolet", "chevrolet monte carlo"], ["122", "fiat", "fiat 124 sport coupe"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["406", "chevrolet", "chevy s-10"], ["209", "pontiac", "pontiac ventura sj"], ["124", "pontiac", "pontiac grand prix"], ["187", "volvo", "volvo 244dl"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:For the cars with 4 cylinders, which model has the largest horsepower?	[["ford"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["285", "peugeot", "peugeot 604sl"], ["216", "plymouth", "plymouth volare premier v8"], ["217", "peugeot", "peugeot 504"], ["59", "peugeot", "peugeot 304"], ["186", "peugeot", "peugeot 504"], ["27", "peugeot", "peugeot 504"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["86", "peugeot", "peugeot 504 (sw)"], ["293", "chevrolet", "chevrolet caprice classic"], ["96", "ford", "ford gran torino"], ["229", "chevrolet", "chevrolet caprice classic"], ["82", "ford", "ford gran torino (sw)"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["245", "chevrolet", "chevrolet chevette"], ["147", "ford", "ford gran torino (sw)"], ["334", "volkswagen", "vw dasher (diesel)"], ["307", "peugeot", "peugeot 504"], ["99", "chevrolet", "chevrolet caprice classic"], ["384", "volkswagen", "volkswagen rabbit l"], ["198", "ford", "ford gran torino"], ["46", "chevrolet", "chevrolet impala"], ["87", "renault", "renault 12 (sw)"], ["356", "toyota", "toyota tercel"], ["338", "renault", "renault lecar deluxe"], ["359", "ford", "ford escort 4w"], ["144", "ford", "ford gran torino"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["262", "ford", "ford fairmont (auto)"], ["194", "renault", "renault 12tl"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["319", "chevrolet", "chevrolet chevette"], ["344", "ford", "ford mustang cobra"], ["340", " volkswagen", "volkswagen rabbit"], ["5", "ford", "ford torino"], ["54", "chevrolet", "chevrolet vega (sw)"], ["288", "pontiac", "pontiac lemans v6"], ["44", "ford", "ford torino 500"], ["123", "chevrolet", "chevrolet monte carlo s"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["205", "volkswagen", "vw rabbit"], ["226", "renault", "renault 5 gtl"], ["78", "chrysler", "chrysler newport royal"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["250", "bmw", "bmw 320i"], ["101", "plymouth", "plymouth fury gran sedan"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["126", "opel", "opel manta"], ["69", "ford", "ford pinto runabout"], ["376", "chevrolet", "chevrolet cavalier"], ["317", "volkswagen", "vw rabbit"], ["18", "ford", "ford mustang boss 302"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["203", "chevrolet", "chevrolet chevette"], ["70", "chevrolet", "chevrolet impala"], ["200", "chevrolet", "chevrolet nova"], ["335", "audi", "audi 5000s (diesel)"], ["290", "ford", "ford fairmont 4"], ["173", "chevrolet", "chevrolet monza 2+2"], ["7", "chevrolet", "chevrolet impala"], ["110", "volkswagen", "volkswagen super beetle"], ["165", "chevrolet", "chevrolet bel air"], ["374", "ford", "ford granada gl"], ["328", "datsun", "datsun 510 hatchback"], ["239", "chrysler", "chrysler cordoba"], ["161", "chevrolet", "chevrolet nova"], ["40", "volkswagen", "volkswagen super beetle 117"], ["111", "chevrolet", "chevrolet impala"], ["141", "chevrolet", "chevrolet chevelle malibu classic"], ["355", "datsun", "datsun 210 mpg"], ["13", "ford", "ford torino (sw)"], ["180", "volkswagen", "volkswagen dasher"], ["305", "mercedes", "mercedes benz 300d"], ["43", "chevrolet", "chevrolet chevelle malibu"], ["174", "ford", "ford mustang ii"], ["19", "chevrolet", "chevrolet monte carlo"], ["318", "toyota", "toyota corolla tercel"], ["274", "chevrolet", "chevrolet chevette"], ["191", "opel", "opel 1900"], ["370", "toyota", "toyota cressida"], ["204", "chevrolet", "chevrolet woody"], ["350", "plymouth", "plymouth reliant"], ["362", "renault", "renault 18i"], ["73", "ford", "ford galaxie 500"], ["357", "mazda", "mazda glc 4"], ["351", "toyota", "toyota starlet"], ["382", "ford", "ford fairmont futura"], ["140", "chevrolet", "chevrolet vega"], ["271", "buick", "buick regal sport coupe (turbo)"], ["145", "buick", "buick century luxus (sw)"], ["48", "ford", "ford galaxie 500"], ["240", "ford", "ford thunderbird"], ["236", "ford", "ford granada"], ["58", "opel", "opel 1900"], ["11", "citroen", "citroen ds-21 pallas"], ["211", "volkswagen", "volkswagen rabbit"], ["84", "volvo", "volvo 145e (sw)"], ["117", "chevrolet", "chevrolet vega"], ["233", "chevrolet", "chevrolet concours"], ["183", "volkswagen", "volkswagen rabbit"], ["169", "chevrolet", "chevrolet chevelle malibu"], ["241", "volkswagen", "volkswagen rabbit custom"], ["343", "triumph", "triumph tr7 coupe"], ["364", "toyota", "toyota corolla"], ["401", "chevrolet", "chevrolet camaro"], ["68", "chevrolet", "chevrolet vega"], ["390", "honda", "honda accord"], ["278", "toyota", "toyota celica gt liftback"], ["375", "chrysler", "chrysler lebaron salon"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["151", "opel", "opel manta"], ["152", "toyota", "toyota corona"], ["306", "cadillac", "cadillac eldorado"], ["406", "chevrolet", "chevy s-10"], ["273", "dodge", "dodge magnum xe"], ["286", "volkswagen", "volkswagen scirocco"], ["176", "ford", "ford pinto"], ["116", "toyota", "toyota carina"], ["208", "ford", "ford granada ghia"], ["272", "ford", "ford futura"], ["124", "pontiac", "pontiac grand prix"], ["325", "audi", "audi 4000"], ["106", "chevrolet", "chevrolet nova custom"], ["39", "ford", "ford pinto"], ["122", "fiat", "fiat 124 sport coupe"], ["50", "dodge", "dodge monaco (sw)"], ["221", "chevrolet", "chevy c10"], ["399", "toyota", "toyota celica gt"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["381", "pontiac", "pontiac phoenix"], ["299", "chevrolet", "chevrolet malibu classic (sw)"], ["316", "pontiac", "pontiac phoenix"], ["120", "ford", "ford pinto"], ["88", "ford", "ford pinto (sw)"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["402", "ford", "ford mustang gl"], ["283", "volvo", "volvo 264gl"], ["37", "chevrolet", "chevrolet vega 2300"], ["33", "chevrolet", "chevy c20"], ["326", "toyota", "toyota corona liftback"], ["30", "bmw", "bmw 2002"], ["150", "volkswagen", "volkswagen dasher"], ["263", "ford", "ford fairmont (man)"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:For all of the 4 cylinder cars, which model has the most horsepower?	[["ford"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["362", "renault", "renault 18i"], ["194", "renault", "renault 12tl"], ["69", "ford", "ford pinto runabout"], ["376", "chevrolet", "chevrolet cavalier"], ["176", "ford", "ford pinto"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["147", "ford", "ford gran torino (sw)"], ["319", "chevrolet", "chevrolet chevette"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["231", "dodge", "dodge monaco brougham"], ["198", "ford", "ford gran torino"], ["144", "ford", "ford gran torino"], ["59", "peugeot", "peugeot 304"], ["262", "ford", "ford fairmont (auto)"], ["44", "ford", "ford torino 500"], ["39", "ford", "ford pinto"], ["120", "ford", "ford pinto"], ["355", "datsun", "datsun 210 mpg"], ["245", "chevrolet", "chevrolet chevette"], ["233", "chevrolet", "chevrolet concours"], ["138", "ford", "ford pinto"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["182", "ford", "ford pinto"], ["363", "honda", "honda prelude"], ["173", "chevrolet", "chevrolet monza 2+2"], ["214", "ford", "ford pinto"], ["203", "chevrolet", "chevrolet chevette"], ["27", "peugeot", "peugeot 504"], ["33", "chevrolet", "chevy c20"], ["216", "plymouth", "plymouth volare premier v8"], ["50", "dodge", "dodge monaco (sw)"], ["13", "ford", "ford torino (sw)"], ["5", "ford", "ford torino"], ["338", "renault", "renault lecar deluxe"], ["60", "fiat", "fiat 124b"], ["190", "fiat", "fiat 131"], ["46", "chevrolet", "chevrolet impala"], ["217", "peugeot", "peugeot 504"], ["360", "ford", "ford escort 2h"], ["125", "fiat", "fiat 128"], ["155", "fiat", "fiat 128"], ["236", "ford", "ford granada"], ["276", "datsun", "datsun 510"], ["82", "ford", "ford gran torino (sw)"], ["96", "ford", "ford gran torino"], ["87", "renault", "renault 12 (sw)"], ["156", "fiat", "fiat 124 tc"], ["277", "dodge", "dodge omni"], ["382", "ford", "ford fairmont futura"], ["165", "chevrolet", "chevrolet bel air"], ["393", "honda", "honda civic (auto)"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["239", "chrysler", "chrysler cordoba"], ["272", "ford", "ford futura"], ["117", "chevrolet", "chevrolet vega"], ["186", "peugeot", "peugeot 504"], ["334", "volkswagen", "vw dasher (diesel)"], ["285", "peugeot", "peugeot 604sl"], ["54", "chevrolet", "chevrolet vega (sw)"], ["340", " volkswagen", "volkswagen rabbit"], ["122", "fiat", "fiat 124 sport coupe"], ["263", "ford", "ford fairmont (man)"], ["7", "chevrolet", "chevrolet impala"], ["193", "dodge", "dodge colt"], ["274", "chevrolet", "chevrolet chevette"], ["191", "opel", "opel 1900"], ["126", "opel", "opel manta"], ["111", "chevrolet", "chevrolet impala"], ["88", "ford", "ford pinto (sw)"], ["226", "renault", "renault 5 gtl"], ["322", "ford", "ford fairmont"], ["369", "volvo", "volvo diesel"], ["30", "bmw", "bmw 2002"], ["205", "volkswagen", "vw rabbit"], ["250", "bmw", "bmw 320i"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["48", "ford", "ford galaxie 500"], ["183", "volkswagen", "volkswagen rabbit"], ["154", "dodge", "dodge colt"], ["380", "dodge", "dodge aries se"], ["370", "toyota", "toyota cressida"], ["196", "dodge", "dodge coronet brougham"], ["273", "dodge", "dodge magnum xe"], ["312", "fiat", "fiat strada custom"], ["307", "peugeot", "peugeot 504"], ["70", "chevrolet", "chevrolet impala"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["353", "honda", "honda civic 1300"], ["211", "volkswagen", "volkswagen rabbit"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["58", "opel", "opel 1900"], ["316", "pontiac", "pontiac phoenix"], ["180", "volkswagen", "volkswagen dasher"], ["331", "dodge", "dodge colt"], ["215", "volvo", "volvo 245"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["371", "datsun", "datsun 810 maxima"], ["174", "ford", "ford mustang ii"], ["384", "volkswagen", "volkswagen rabbit l"], ["11", "citroen", "citroen ds-21 pallas"], ["359", "ford", "ford escort 4w"], ["356", "toyota", "toyota tercel"], ["229", "chevrolet", "chevrolet caprice classic"], ["151", "opel", "opel manta"], ["37", "chevrolet", "chevrolet vega 2300"], ["249", "datsun", "datsun 810"], ["128", "volvo", "volvo 144ea"], ["311", "datsun", "datsun 210"], ["18", "ford", "ford mustang boss 302"], ["283", "volvo", "volvo 264gl"], ["181", "datsun", "datsun 710"], ["374", "ford", "ford granada gl"], ["317", "volkswagen", "vw rabbit"], ["36", "datsun", "datsun pl510"], ["351", "toyota", "toyota starlet"], ["288", "pontiac", "pontiac lemans v6"], ["134", "ford", "ford maverick"], ["293", "chevrolet", "chevrolet caprice classic"], ["136", "chevrolet", "chevrolet nova"], ["332", "datsun", "datsun 210"], ["248", "volkswagen", "volkswagen dasher"], ["123", "chevrolet", "chevrolet monte carlo s"], ["19", "chevrolet", "chevrolet monte carlo"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["258", "dodge", "dodge diplomat"], ["73", "ford", "ford galaxie 500"], ["344", "ford", "ford mustang cobra"], ["320", "datsun", "datsun 310"], ["286", "volkswagen", "volkswagen scirocco"], ["328", "datsun", "datsun 510 hatchback"], ["350", "plymouth", "plymouth reliant"], ["157", "honda", "honda civic"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["354", "subaru", "subaru"], ["294", "ford", "ford ltd landau"], ["398", "ford", "ford granada l"], ["178", "pontiac", "pontiac astro"], ["392", "honda", "honda civic"], ["228", "datsun", "datsun f-10 hatchback"], ["62", "datsun", "datsun 1200"], ["2", "buick", "buick skylark 320"], ["25", "datsun", "datsun pl510"], ["100", "ford", "ford ltd"], ["187", "volvo", "volvo 244dl"], ["40", "volkswagen", "volkswagen super beetle 117"], ["140", "chevrolet", "chevrolet vega"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Among the cars with more than lowest horsepower, which ones do not have more than 3 cylinders? List the car makeid and make name.	[["79", "mazda rx2 coupe"], ["119", "mazda rx3"], ["251", "mazda rx-4"]]	2,048	Answer:
Table car_names: [["MakeId", "Model", "Make"], ["362", "renault", "renault 18i"], ["194", "renault", "renault 12tl"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["376", "chevrolet", "chevrolet cavalier"], ["319", "chevrolet", "chevrolet chevette"], ["186", "peugeot", "peugeot 504"], ["285", "peugeot", "peugeot 604sl"], ["59", "peugeot", "peugeot 304"], ["87", "renault", "renault 12 (sw)"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["384", "volkswagen", "volkswagen rabbit l"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["334", "volkswagen", "vw dasher (diesel)"], ["203", "chevrolet", "chevrolet chevette"], ["245", "chevrolet", "chevrolet chevette"], ["226", "renault", "renault 5 gtl"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["338", "renault", "renault lecar deluxe"], ["216", "plymouth", "plymouth volare premier v8"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["69", "ford", "ford pinto runabout"], ["46", "chevrolet", "chevrolet impala"], ["27", "peugeot", "peugeot 504"], ["233", "chevrolet", "chevrolet concours"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["217", "peugeot", "peugeot 504"], ["54", "chevrolet", "chevrolet vega (sw)"], ["328", "datsun", "datsun 510 hatchback"], ["111", "chevrolet", "chevrolet impala"], ["173", "chevrolet", "chevrolet monza 2+2"], ["374", "ford", "ford granada gl"], ["250", "bmw", "bmw 320i"], ["293", "chevrolet", "chevrolet caprice classic"], ["5", "ford", "ford torino"], ["340", " volkswagen", "volkswagen rabbit"], ["60", "fiat", "fiat 124b"], ["117", "chevrolet", "chevrolet vega"], ["50", "dodge", "dodge monaco (sw)"], ["44", "ford", "ford torino 500"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["335", "audi", "audi 5000s (diesel)"], ["262", "ford", "ford fairmont (auto)"], ["86", "peugeot", "peugeot 504 (sw)"], ["198", "ford", "ford gran torino"], ["229", "chevrolet", "chevrolet caprice classic"], ["180", "volkswagen", "volkswagen dasher"], ["156", "fiat", "fiat 124 tc"], ["183", "volkswagen", "volkswagen rabbit"], ["138", "ford", "ford pinto"], ["307", "peugeot", "peugeot 504"], ["274", "chevrolet", "chevrolet chevette"], ["370", "toyota", "toyota cressida"], ["147", "ford", "ford gran torino (sw)"], ["359", "ford", "ford escort 4w"], ["356", "toyota", "toyota tercel"], ["33", "chevrolet", "chevy c20"], ["355", "datsun", "datsun 210 mpg"], ["286", "volkswagen", "volkswagen scirocco"], ["7", "chevrolet", "chevrolet impala"], ["176", "ford", "ford pinto"], ["37", "chevrolet", "chevrolet vega 2300"], ["165", "chevrolet", "chevrolet bel air"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["123", "chevrolet", "chevrolet monte carlo s"], ["205", "volkswagen", "vw rabbit"], ["351", "toyota", "toyota starlet"], ["96", "ford", "ford gran torino"], ["317", "volkswagen", "vw rabbit"], ["11", "citroen", "citroen ds-21 pallas"], ["13", "ford", "ford torino (sw)"], ["231", "dodge", "dodge monaco brougham"], ["248", "volkswagen", "volkswagen dasher"], ["125", "fiat", "fiat 128"], ["382", "ford", "ford fairmont futura"], ["18", "ford", "ford mustang boss 302"], ["236", "ford", "ford granada"], ["239", "chrysler", "chrysler cordoba"], ["182", "ford", "ford pinto"], ["122", "fiat", "fiat 124 sport coupe"], ["99", "chevrolet", "chevrolet caprice classic"], ["214", "ford", "ford pinto"], ["39", "ford", "ford pinto"], ["276", "datsun", "datsun 510"], ["40", "volkswagen", "volkswagen super beetle 117"], ["211", "volkswagen", "volkswagen rabbit"], ["241", "volkswagen", "volkswagen rabbit custom"], ["380", "dodge", "dodge aries se"], ["136", "chevrolet", "chevrolet nova"], ["360", "ford", "ford escort 2h"], ["200", "chevrolet", "chevrolet nova"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["19", "chevrolet", "chevrolet monte carlo"], ["82", "ford", "ford gran torino (sw)"], ["283", "volvo", "volvo 264gl"], ["316", "pontiac", "pontiac phoenix"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["88", "ford", "ford pinto (sw)"], ["120", "ford", "ford pinto"], ["155", "fiat", "fiat 128"], ["70", "chevrolet", "chevrolet impala"], ["36", "datsun", "datsun pl510"], ["126", "opel", "opel manta"], ["190", "fiat", "fiat 131"], ["228", "datsun", "datsun f-10 hatchback"], ["394", "datsun", "datsun 310 gx"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["369", "volvo", "volvo diesel"], ["144", "ford", "ford gran torino"], ["215", "volvo", "volvo 245"], ["401", "chevrolet", "chevrolet camaro"], ["68", "chevrolet", "chevrolet vega"], ["288", "pontiac", "pontiac lemans v6"], ["181", "datsun", "datsun 710"], ["128", "volvo", "volvo 144ea"], ["406", "chevrolet", "chevy s-10"], ["169", "chevrolet", "chevrolet chevelle malibu"], ["191", "opel", "opel 1900"], ["337", "honda", "honda civic 1500 gl"], ["48", "ford", "ford galaxie 500"], ["84", "volvo", "volvo 145e (sw)"], ["150", "volkswagen", "volkswagen dasher"], ["273", "dodge", "dodge magnum xe"], ["110", "volkswagen", "volkswagen super beetle"], ["25", "datsun", "datsun pl510"], ["318", "toyota", "toyota corolla tercel"], ["272", "ford", "ford futura"], ["218", "toyota", "toyota mark ii"], ["161", "chevrolet", "chevrolet nova"], ["354", "subaru", "subaru"], ["152", "toyota", "toyota corona"], ["178", "pontiac", "pontiac astro"], ["62", "datsun", "datsun 1200"], ["52", "pontiac", "pontiac safari (sw)"], ["361", "volkswagen", "volkswagen jetta"], ["311", "datsun", "datsun 210"], ["116", "toyota", "toyota carina"], ["101", "plymouth", "plymouth fury gran sedan"], ["312", "fiat", "fiat strada custom"], ["325", "audi", "audi 4000"], ["185", "audi", "audi 100ls"], ["344", "ford", "ford mustang cobra"], ["277", "dodge", "dodge omni"], ["38", "toyota", "toyota corona"], ["1", "chevrolet", "chevrolet chevelle malibu"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Among the cars that do not have the minimum horsepower , what are the make ids and names of all those with less than 4 cylinders ?	[["79", "mazda rx2 coupe"], ["119", "mazda rx3"], ["251", "mazda rx-4"]]	2,048	Answer:
Table cars_data: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["8", "14", "8", "440.0", "215", "4312", "8.5", "1970"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["88", "22", "4", "122.0", "86", "2395", "16.0", "1972"], ["82", "13", "8", "302.0", "140", "4294", "16.0", "1972"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["340", "29.8", "4", "89.0", "62", "1845", "15.3", "1980"], ["17", "14", "8", "340.0", "160", "3609", "8.0", "1970"], ["318", "38.1", "4", "89.0", "60", "1968", "18.8", "1980"], ["296", "18.2", "8", "318.0", "135", "3830", "15.2", "1979"], ["293", "17", "8", "305.0", "130", "3840", "15.4", "1979"], ["124", "16", "8", "400.0", "230", "4278", "9.5", "1973"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["147", "14", "8", "302.0", "140", "4638", "16.0", "1974"], ["112", "12", "8", "400.0", "167", "4906", "12.5", "1973"], ["108", "18", "6", "250.0", "88", "3021", "16.5", "1973"], ["81", "13", "8", "307.0", "130", "4098", "14.0", "1972"], ["229", "17.5", "8", "305.0", "145", "3880", "12.5", "1977"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["164", "16", "8", "400.0", "170", "4668", "11.5", "1975"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["166", "16", "8", "318.0", "150", "4498", "14.5", "1975"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["77", "12", "8", "350.0", "160", "4456", "13.5", "1972"], ["141", "16", "6", "250.0", "100", "3781", "17.0", "1974"], ["327", "31.3", "4", "120.0", "75", "2542", "17.5", "1980"], ["106", "16", "6", "250.0", "100", "3278", "18.0", "1973"], ["80", "15", "8", "304.0", "150", "3892", "12.5", "1972"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["18", "null", "8", "302.0", "140", "3353", "8.0", "1970"], ["47", "14", "8", "400.0", "175", "4464", "11.5", "1971"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["332", "40.8", "4", "85.0", "65", "2110", "19.2", "1980"], ["94", "14", "8", "304.0", "150", "3672", "11.5", "1973"], ["71", "14", "8", "400.0", "175", "4385", "12.0", "1972"], ["78", "13", "8", "400.0", "190", "4422", "12.5", "1972"], ["84", "18", "4", "121.0", "112", "2933", "14.5", "1972"], ["273", "17.5", "8", "318.0", "140", "4080", "13.7", "1978"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["337", "44.6", "4", "91.0", "67", "1850", "13.8", "1980"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["16", "15", "8", "383.0", "170", "3563", "10.0", "1970"], ["285", "16.2", "6", "163.0", "133", "3410", "15.8", "1978"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["339", "33.8", "4", "97.0", "67", "2145", "18.0", "1980"], ["341", "32.7", "6", "168.0", "132", "2910", "11.4", "1980"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["217", "19", "4", "120.0", "88", "3270", "21.9", "1976"], ["3", "18", "8", "318.0", "150", "3436", "11.0", "1970"], ["326", "29.8", "4", "134.0", "90", "2711", "15.5", "1980"], ["97", "15", "8", "318.0", "150", "3777", "12.5", "1973"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the maximum miles per gallon of the car with 8 cylinders or produced before 1980 ?	[["null"]]	2,048	Answer:
Table cars_data: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["328", "37", "4", "119.0", "92", "2434", "15.0", "1980"], ["306", "23", "8", "350.0", "125", "3900", "17.4", "1979"], ["8", "14", "8", "440.0", "215", "4312", "8.5", "1970"], ["380", "29", "4", "135.0", "84", "2525", "16.0", "1982"], ["378", "34", "4", "112.0", "88", "2395", "18.0", "1982"], ["318", "38.1", "4", "89.0", "60", "1968", "18.8", "1980"], ["343", "35", "4", "122.0", "88", "2500", "15.1", "1980"], ["83", "14", "8", "318.0", "150", "4077", "14.0", "1972"], ["293", "17", "8", "305.0", "130", "3840", "15.4", "1979"], ["208", "18", "6", "250.0", "78", "3574", "21.0", "1976"], ["46", "14", "8", "350.0", "165", "4209", "12.0", "1971"], ["88", "22", "4", "122.0", "86", "2395", "16.0", "1972"], ["114", "12", "8", "350.0", "180", "4499", "12.5", "1973"], ["144", "16", "8", "302.0", "140", "4141", "14.0", "1974"], ["145", "13", "8", "350.0", "150", "4699", "14.5", "1974"], ["296", "18.2", "8", "318.0", "135", "3830", "15.2", "1979"], ["95", "13", "8", "350.0", "145", "3988", "13.0", "1973"], ["129", "15", "8", "318.0", "150", "3399", "11.0", "1973"], ["123", "15", "8", "350.0", "145", "4082", "13.0", "1973"], ["377", "27", "4", "112.0", "88", "2640", "18.6", "1982"], ["340", "29.8", "4", "89.0", "62", "1845", "15.3", "1980"], ["82", "13", "8", "302.0", "140", "4294", "16.0", "1972"], ["297", "16.9", "8", "350.0", "155", "4360", "14.9", "1979"], ["70", "13", "8", "350.0", "165", "4274", "12.0", "1972"], ["108", "18", "6", "250.0", "88", "3021", "16.5", "1973"], ["112", "12", "8", "400.0", "167", "4906", "12.5", "1973"], ["258", "19.4", "8", "318.0", "140", "3735", "13.2", "1978"], ["376", "28", "4", "112.0", "88", "2605", "19.6", "1982"], ["124", "16", "8", "400.0", "230", "4278", "9.5", "1973"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"], ["17", "14", "8", "340.0", "160", "3609", "8.0", "1970"], ["326", "29.8", "4", "134.0", "90", "2711", "15.5", "1980"], ["165", "15", "8", "350.0", "145", "4440", "14.0", "1975"], ["290", "22.3", "4", "140.0", "88", "2890", "17.3", "1979"], ["341", "32.7", "6", "168.0", "132", "2910", "11.4", "1980"], ["80", "15", "8", "304.0", "150", "3892", "12.5", "1972"], ["327", "31.3", "4", "120.0", "75", "2542", "17.5", "1980"], ["132", "11", "8", "350.0", "180", "3664", "11.0", "1973"], ["93", "13", "8", "350.0", "175", "4100", "13.0", "1973"], ["121", "21", "6", "155.0", "107", "2472", "14.0", "1973"], ["337", "44.6", "4", "91.0", "67", "1850", "13.8", "1980"], ["229", "17.5", "8", "305.0", "145", "3880", "12.5", "1977"], ["101", "14", "8", "318.0", "150", "4237", "14.5", "1973"], ["106", "16", "6", "250.0", "100", "3278", "18.0", "1973"], ["72", "15", "8", "318.0", "150", "4135", "13.5", "1972"], ["217", "19", "4", "120.0", "88", "3270", "21.9", "1976"], ["77", "12", "8", "350.0", "160", "4456", "13.5", "1972"], ["81", "13", "8", "307.0", "130", "4098", "14.0", "1972"], ["55", "19", "6", "250.0", "100", "3282", "15.0", "1971"], ["273", "17.5", "8", "318.0", "140", "4080", "13.7", "1978"], ["56", "18", "6", "250.0", "88", "3139", "14.5", "1971"], ["240", "16", "8", "351.0", "149", "4335", "14.5", "1977"], ["94", "14", "8", "304.0", "150", "3672", "11.5", "1973"], ["7", "14", "8", "454.0", "220", "4354", "9.0", "1970"], ["98", "12", "8", "429.0", "198", "4952", "11.5", "1973"], ["140", "25", "4", "140.0", "75", "2542", "17.0", "1974"], ["47", "14", "8", "400.0", "175", "4464", "11.5", "1971"], ["164", "16", "8", "400.0", "170", "4668", "11.5", "1975"], ["322", "26.4", "4", "140.0", "88", "2870", "18.1", "1980"], ["162", "15", "6", "250.0", "72", "3432", "21.0", "1975"], ["45", "18", "6", "232.0", "100", "3288", "15.5", "1971"], ["325", "34.3", "4", "97.0", "78", "2188", "15.8", "1980"], ["294", "17.6", "8", "302.0", "129", "3725", "13.4", "1979"], ["147", "14", "8", "302.0", "140", "4638", "16.0", "1974"], ["332", "40.8", "4", "85.0", "65", "2110", "19.2", "1980"], ["356", "37.7", "4", "89.0", "62", "2050", "17.3", "1981"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the maximum mpg of the cars that had 8 cylinders or that were produced before 1980 ?	[["null"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["359", "ford", "ford escort 4w"], ["240", "ford", "ford thunderbird"], ["73", "ford", "ford galaxie 500"], ["374", "ford", "ford granada gl"], ["382", "ford", "ford fairmont futura"], ["262", "ford", "ford fairmont (auto)"], ["236", "ford", "ford granada"], ["32", "ford", "ford f250"], ["290", "ford", "ford fairmont 4"], ["294", "ford", "ford ltd landau"], ["208", "ford", "ford granada ghia"], ["174", "ford", "ford mustang ii"], ["398", "ford", "ford granada l"], ["244", "ford", "ford mustang ii 2+2"], ["82", "ford", "ford gran torino (sw)"], ["44", "ford", "ford torino 500"], ["69", "ford", "ford pinto runabout"], ["48", "ford", "ford galaxie 500"], ["167", "ford", "ford ltd"], ["88", "ford", "ford pinto (sw)"], ["176", "ford", "ford pinto"], ["214", "ford", "ford pinto"], ["120", "ford", "ford pinto"], ["147", "ford", "ford gran torino (sw)"], ["272", "ford", "ford futura"], ["100", "ford", "ford ltd"], ["39", "ford", "ford pinto"], ["360", "ford", "ford escort 2h"], ["405", "ford", "ford ranger"], ["13", "ford", "ford torino (sw)"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["298", "ford", "ford country squire (sw)"], ["222", "ford", "ford f108"], ["51", "ford", "ford country squire (sw)"], ["96", "ford", "ford gran torino"], ["6", "ford", "ford galaxie 500"], ["350", "plymouth", "plymouth reliant"], ["138", "ford", "ford pinto"], ["117", "chevrolet", "chevrolet vega"], ["198", "ford", "ford gran torino"], ["182", "ford", "ford pinto"], ["245", "chevrolet", "chevrolet chevette"], ["263", "ford", "ford fairmont (man)"], ["344", "ford", "ford mustang cobra"], ["93", "buick", "buick century 350"], ["322", "ford", "ford fairmont"], ["301", "volkswagen", "vw rabbit custom"], ["402", "ford", "ford mustang gl"], ["24", "ford", "ford maverick"], ["144", "ford", "ford gran torino"], ["54", "chevrolet", "chevrolet vega (sw)"], ["253", "ford", "ford fiesta"], ["286", "volkswagen", "volkswagen scirocco"], ["134", "ford", "ford maverick"], ["355", "datsun", "datsun 210 mpg"], ["163", "ford", "ford maverick"], ["334", "volkswagen", "vw dasher (diesel)"], ["63", "volkswagen", "volkswagen model 111"], ["145", "buick", "buick century luxus (sw)"], ["37", "chevrolet", "chevrolet vega 2300"], ["270", "chevrolet", "chevrolet monte carlo landau"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["5", "ford", "Ford Motor Company", "1"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["335", "audi", "audi 5000s (diesel)"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["18", "ford", "ford mustang boss 302"], ["205", "volkswagen", "vw rabbit"], ["340", " volkswagen", "volkswagen rabbit"], ["376", "chevrolet", "chevrolet cavalier"], ["108", "ford", "ford maverick"], ["5", "ford", "ford torino"], ["338", "renault", "renault lecar deluxe"], ["201", "ford", "ford maverick"], ["216", "plymouth", "plymouth volare premier v8"], ["36", "datsun", "datsun pl510"], ["87", "renault", "renault 12 (sw)"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["228", "datsun", "datsun f-10 hatchback"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["241", "volkswagen", "volkswagen rabbit custom"], ["67", "volkswagen", "volkswagen type 3"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["40", "volkswagen", "volkswagen super beetle 117"], ["249", "datsun", "datsun 810"], ["371", "datsun", "datsun 810 maxima"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["317", "volkswagen", "vw rabbit"], ["215", "volvo", "volvo 245"], ["288", "pontiac", "pontiac lemans v6"], ["165", "chevrolet", "chevrolet bel air"], ["361", "volkswagen", "volkswagen jetta"], ["221", "chevrolet", "chevy c10"], ["370", "toyota", "toyota cressida"], ["173", "chevrolet", "chevrolet monza 2+2"], ["283", "volvo", "volvo 264gl"], ["140", "chevrolet", "chevrolet vega"], ["369", "volvo", "volvo diesel"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["362", "renault", "renault 18i"], ["384", "volkswagen", "volkswagen rabbit l"], ["68", "chevrolet", "chevrolet vega"], ["328", "datsun", "datsun 510 hatchback"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["112", "ford", "ford country"], ["33", "chevrolet", "chevy c20"], ["239", "chrysler", "chrysler cordoba"], ["90", "toyota", "toyota corona mark ii (sw)"], ["394", "datsun", "datsun 310 gx"], ["89", "datsun", "datsun 510 (sw)"], ["203", "chevrolet", "chevrolet chevette"], ["180", "volkswagen", "volkswagen dasher"], ["389", "nissan", "nissan stanza xe"], ["297", "buick", "buick estate wagon (sw)"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["95", "chevrolet", "chevrolet malibu"], ["325", "audi", "audi 4000"], ["233", "chevrolet", "chevrolet concours"], ["379", "pontiac", "pontiac j2000 se hatchback"], ["226", "renault", "renault 5 gtl"], ["319", "chevrolet", "chevrolet chevette"], ["56", "ford", "ford mustang"], ["161", "chevrolet", "chevrolet nova"], ["271", "buick", "buick regal sport coupe (turbo)"], ["351", "toyota", "toyota starlet"], ["43", "chevrolet", "chevrolet chevelle malibu"], ["403", "volkswagen", "vw pickup"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["274", "chevrolet", "chevrolet chevette"], ["231", "dodge", "dodge monaco brougham"], ["101", "plymouth", "plymouth fury gran sedan"], ["326", "toyota", "toyota corona liftback"], ["212", "datsun", "datsun b-210"], ["225", "buick", "buick opel isuzu deluxe"], ["159", "fiat", "fiat x1.9"], ["52", "pontiac", "pontiac safari (sw)"], ["58", "opel", "opel 1900"], ["25", "datsun", "datsun pl510"], ["19", "chevrolet", "chevrolet monte carlo"], ["399", "toyota", "toyota celica gt"], ["123", "chevrolet", "chevrolet monte carlo s"], ["150", "volkswagen", "volkswagen dasher"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Which models are lighter than 3500 but not built by the 'Ford Motor Company'?	[["plymouth"], ["amc"], ["citroen"], ["buick"], ["toyota"], ["datsun"], ["volkswagen"], ["peugeot"], ["audi"], ["saab"], ["bmw"], ["chevrolet"], ["pontiac"], ["opel"], ["fiat"], ["dodge"], ["mazda"], ["volvo"], ["renault"], ["honda"], ["subaru"], ["oldsmobile"], ["mercedes-benz"], ["triumph"], ["chrysler"], ["nissan"]]	2,048	Answer:
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["359", "ford", "ford escort 4w"], ["240", "ford", "ford thunderbird"], ["73", "ford", "ford galaxie 500"], ["262", "ford", "ford fairmont (auto)"], ["382", "ford", "ford fairmont futura"], ["32", "ford", "ford f250"], ["374", "ford", "ford granada gl"], ["236", "ford", "ford granada"], ["290", "ford", "ford fairmont 4"], ["294", "ford", "ford ltd landau"], ["398", "ford", "ford granada l"], ["48", "ford", "ford galaxie 500"], ["174", "ford", "ford mustang ii"], ["69", "ford", "ford pinto runabout"], ["208", "ford", "ford granada ghia"], ["244", "ford", "ford mustang ii 2+2"], ["214", "ford", "ford pinto"], ["167", "ford", "ford ltd"], ["176", "ford", "ford pinto"], ["272", "ford", "ford futura"], ["82", "ford", "ford gran torino (sw)"], ["44", "ford", "ford torino 500"], ["88", "ford", "ford pinto (sw)"], ["39", "ford", "ford pinto"], ["120", "ford", "ford pinto"], ["222", "ford", "ford f108"], ["138", "ford", "ford pinto"], ["405", "ford", "ford ranger"], ["182", "ford", "ford pinto"], ["360", "ford", "ford escort 2h"], ["263", "ford", "ford fairmont (man)"], ["100", "ford", "ford ltd"], ["6", "ford", "ford galaxie 500"], ["147", "ford", "ford gran torino (sw)"], ["322", "ford", "ford fairmont"], ["96", "ford", "ford gran torino"], ["298", "ford", "ford country squire (sw)"], ["93", "buick", "buick century 350"], ["198", "ford", "ford gran torino"], ["13", "ford", "ford torino (sw)"], ["51", "ford", "ford country squire (sw)"], ["344", "ford", "ford mustang cobra"], ["350", "plymouth", "plymouth reliant"], ["245", "chevrolet", "chevrolet chevette"], ["402", "ford", "ford mustang gl"], ["117", "chevrolet", "chevrolet vega"], ["144", "ford", "ford gran torino"], ["253", "ford", "ford fiesta"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["24", "ford", "ford maverick"], ["134", "ford", "ford maverick"], ["163", "ford", "ford maverick"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["5", "ford", "Ford Motor Company", "1"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["54", "chevrolet", "chevrolet vega (sw)"], ["145", "buick", "buick century luxus (sw)"], ["286", "volkswagen", "volkswagen scirocco"], ["355", "datsun", "datsun 210 mpg"], ["301", "volkswagen", "vw rabbit custom"], ["37", "chevrolet", "chevrolet vega 2300"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["18", "ford", "ford mustang boss 302"], ["201", "ford", "ford maverick"], ["108", "ford", "ford maverick"], ["376", "chevrolet", "chevrolet cavalier"], ["5", "ford", "ford torino"], ["334", "volkswagen", "vw dasher (diesel)"], ["112", "ford", "ford country"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["288", "pontiac", "pontiac lemans v6"], ["228", "datsun", "datsun f-10 hatchback"], ["63", "volkswagen", "volkswagen model 111"], ["36", "datsun", "datsun pl510"], ["221", "chevrolet", "chevy c10"], ["249", "datsun", "datsun 810"], ["340", " volkswagen", "volkswagen rabbit"], ["338", "renault", "renault lecar deluxe"], ["335", "audi", "audi 5000s (diesel)"], ["216", "plymouth", "plymouth volare premier v8"], ["205", "volkswagen", "vw rabbit"], ["165", "chevrolet", "chevrolet bel air"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["371", "datsun", "datsun 810 maxima"], ["203", "chevrolet", "chevrolet chevette"], ["297", "buick", "buick estate wagon (sw)"], ["33", "chevrolet", "chevy c20"], ["56", "ford", "ford mustang"], ["233", "chevrolet", "chevrolet concours"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["241", "volkswagen", "volkswagen rabbit custom"], ["379", "pontiac", "pontiac j2000 se hatchback"], ["68", "chevrolet", "chevrolet vega"], ["239", "chrysler", "chrysler cordoba"], ["140", "chevrolet", "chevrolet vega"], ["271", "buick", "buick regal sport coupe (turbo)"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["67", "volkswagen", "volkswagen type 3"], ["283", "volvo", "volvo 264gl"], ["173", "chevrolet", "chevrolet monza 2+2"], ["319", "chevrolet", "chevrolet chevette"], ["215", "volvo", "volvo 245"], ["394", "datsun", "datsun 310 gx"], ["87", "renault", "renault 12 (sw)"], ["361", "volkswagen", "volkswagen jetta"], ["95", "chevrolet", "chevrolet malibu"], ["317", "volkswagen", "vw rabbit"], ["231", "dodge", "dodge monaco brougham"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["40", "volkswagen", "volkswagen super beetle 117"], ["274", "chevrolet", "chevrolet chevette"], ["369", "volvo", "volvo diesel"], ["328", "datsun", "datsun 510 hatchback"], ["161", "chevrolet", "chevrolet nova"], ["225", "buick", "buick opel isuzu deluxe"], ["325", "audi", "audi 4000"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["347", "buick", "buick skylark"], ["164", "pontiac", "pontiac catalina"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["384", "volkswagen", "volkswagen rabbit l"], ["395", "buick", "buick century limited"], ["370", "toyota", "toyota cressida"], ["89", "datsun", "datsun 510 (sw)"], ["362", "renault", "renault 18i"], ["349", "chevrolet", "chevrolet citation"], ["372", "buick", "buick century"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["212", "datsun", "datsun b-210"], ["178", "pontiac", "pontiac astro"], ["43", "chevrolet", "chevrolet chevelle malibu"], ["180", "volkswagen", "volkswagen dasher"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["308", "oldsmobile", "oldsmobile cutlass salon brougham"], ["389", "nissan", "nissan stanza xe"], ["58", "opel", "opel 1900"], ["101", "plymouth", "plymouth fury gran sedan"], ["403", "volkswagen", "vw pickup"], ["351", "toyota", "toyota starlet"], ["19", "chevrolet", "chevrolet monte carlo"], ["123", "chevrolet", "chevrolet monte carlo s"], ["90", "toyota", "toyota corona mark ii (sw)"], ["326", "toyota", "toyota corona liftback"], ["191", "opel", "opel 1900"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the different models wthat are lighter than 3500 but were not built by the Ford Motor Company?	[["plymouth"], ["amc"], ["citroen"], ["buick"], ["toyota"], ["datsun"], ["volkswagen"], ["peugeot"], ["audi"], ["saab"], ["bmw"], ["chevrolet"], ["pontiac"], ["opel"], ["fiat"], ["dodge"], ["mazda"], ["volvo"], ["renault"], ["honda"], ["subaru"], ["oldsmobile"], ["mercedes-benz"], ["triumph"], ["chrysler"], ["nissan"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["8", "nissan", "Nissan Motors", "4"], ["13", "daimler benz", "Daimler Benz", "2"], ["22", "kia", "Kia Motors", "8"], ["4", "gm", "General Motors", "1"], ["16", "renault", "Renault", "3"], ["2", "volkswagen", "Volkswagen", "2"], ["21", "volvo", "Volvo", "6"]]Table countries: [["CountryId", "CountryName", "Continent"], ["2", "germany", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["19", "toyota", "Toyota", "4"], ["7", "citroen", "Citroen", "3"]]Table countries: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["23", "hyundai", "Hyundai", "8"]]Table countries: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"], ["3", "bmw", "BMW", "2"], ["1", "amc", "American Motor Company", "1"]]Table countries: [["CountryId", "CountryName", "Continent"], ["3", "france", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"], ["11", "honda", "Honda", "4"]]Table countries: [["CountryId", "CountryName", "Continent"], ["5", "italy", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["5", "ford", "Ford Motor Company", "1"], ["18", "subaru", "Subaru", "4"], ["12", "mazda", "Mazda", "4"], ["6", "chrysler", "Chrysler", "1"]]Table countries: [["CountryId", "CountryName", "Continent"], ["6", "sweden", "2"], ["4", "japan", "3"], ["8", "korea", "3"], ["12", "new zealand", "5"], ["14", "mexico", "1"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table countries: [["CountryId", "CountryName", "Continent"], ["10", "nigeria", "4"], ["13", "egypt", "4"], ["11", "australia", "5"], ["7", "uk", "2"], ["1", "usa", "1"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"], ["20", "triumph", "Triumph", "7"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the name of the countries where there is not a single car maker?	[["australia"], ["brazil"], ["egypt"], ["mexico"], ["new zealand"], ["nigeria"], ["russia"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"], ["8", "nissan", "Nissan Motors", "4"], ["22", "kia", "Kia Motors", "8"]]Table countries: [["CountryId", "CountryName", "Continent"], ["2", "germany", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["16", "renault", "Renault", "3"], ["7", "citroen", "Citroen", "3"]]Table countries: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"], ["3", "france", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"], ["2", "volkswagen", "Volkswagen", "2"], ["21", "volvo", "Volvo", "6"]]Table countries: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"], ["5", "italy", "2"], ["8", "korea", "3"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["23", "hyundai", "Hyundai", "8"]]Table countries: [["CountryId", "CountryName", "Continent"], ["12", "new zealand", "5"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"]]Table countries: [["CountryId", "CountryName", "Continent"], ["4", "japan", "3"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"]]Table countries: [["CountryId", "CountryName", "Continent"], ["6", "sweden", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["3", "bmw", "BMW", "2"], ["19", "toyota", "Toyota", "4"], ["11", "honda", "Honda", "4"]]Table countries: [["CountryId", "CountryName", "Continent"], ["14", "mexico", "1"], ["13", "egypt", "4"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["5", "ford", "Ford Motor Company", "1"]]Table countries: [["CountryId", "CountryName", "Continent"], ["10", "nigeria", "4"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["6", "chrysler", "Chrysler", "1"], ["14", "opel", "Opel", "2"], ["17", "saab", "Saab", "6"]]Table countries: [["CountryId", "CountryName", "Continent"], ["11", "australia", "5"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["18", "subaru", "Subaru", "4"], ["12", "mazda", "Mazda", "4"]]Table countries: [["CountryId", "CountryName", "Continent"], ["7", "uk", "2"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"]]Table countries: [["CountryId", "CountryName", "Continent"], ["1", "usa", "1"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the names of the countries with no car makers?	[["australia"], ["brazil"], ["egypt"], ["mexico"], ["new zealand"], ["nigeria"], ["russia"]]	2,048	Answer:
Table car_makers: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"], ["8", "nissan", "Nissan Motors", "4"], ["16", "renault", "Renault", "3"]]Table car_names: [["MakeId", "Model", "Make"], ["338", "renault", "renault lecar deluxe"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"]]Table car_names: [["MakeId", "Model", "Make"], ["362", "renault", "renault 18i"], ["173", "chevrolet", "chevrolet monza 2+2"]]Table model_list: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"]]Table car_names: [["MakeId", "Model", "Make"], ["87", "renault", "renault 12 (sw)"], ["270", "chevrolet", "chevrolet monte carlo landau"]]Table model_list: [["ModelId", "Maker", "Model"], ["26", "16", "renault"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["2", "volkswagen", "Volkswagen", "2"], ["1", "amc", "American Motor Company", "1"]]Table car_names: [["MakeId", "Model", "Make"], ["334", "volkswagen", "vw dasher (diesel)"], ["319", "chevrolet", "chevrolet chevette"], ["239", "chrysler", "chrysler cordoba"], ["333", "volkswagen", "vw rabbit c (diesel)"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["22", "kia", "Kia Motors", "8"]]Table model_list: [["ModelId", "Maker", "Model"], ["23", "15", "peugeot"]]Table car_names: [["MakeId", "Model", "Make"], ["200", "chevrolet", "chevrolet nova"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["245", "chevrolet", "chevrolet chevette"], ["194", "renault", "renault 12tl"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["393", "honda", "honda civic (auto)"], ["221", "chevrolet", "chevy c10"], ["283", "volvo", "volvo 264gl"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["233", "chevrolet", "chevrolet concours"], ["241", "volkswagen", "volkswagen rabbit custom"], ["376", "chevrolet", "chevrolet cavalier"], ["180", "volkswagen", "volkswagen dasher"], ["21", "toyota", "toyota corona mark ii"], ["356", "toyota", "toyota tercel"], ["218", "toyota", "toyota mark ii"], ["294", "ford", "ford ltd landau"], ["167", "ford", "ford ltd"], ["340", " volkswagen", "volkswagen rabbit"], ["165", "chevrolet", "chevrolet bel air"], ["205", "volkswagen", "vw rabbit"], ["54", "chevrolet", "chevrolet vega (sw)"], ["231", "dodge", "dodge monaco brougham"], ["78", "chrysler", "chrysler newport royal"], ["147", "ford", "ford gran torino (sw)"], ["7", "chevrolet", "chevrolet impala"], ["82", "ford", "ford gran torino (sw)"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["33", "chevrolet", "chevy c20"], ["176", "ford", "ford pinto"], ["126", "opel", "opel manta"], ["148", "amc", "amc matador (sw)"], ["286", "volkswagen", "volkswagen scirocco"], ["203", "chevrolet", "chevrolet chevette"], ["27", "peugeot", "peugeot 504"], ["136", "chevrolet", "chevrolet nova"], ["100", "ford", "ford ltd"], ["293", "chevrolet", "chevrolet caprice classic"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["262", "ford", "ford fairmont (auto)"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["211", "volkswagen", "volkswagen rabbit"], ["161", "chevrolet", "chevrolet nova"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["183", "volkswagen", "volkswagen rabbit"], ["151", "opel", "opel manta"]]Table model_list: [["ModelId", "Maker", "Model"], ["2", "2", "audi"], ["3", "3", "bmw"]]Table car_names: [["MakeId", "Model", "Make"], ["144", "ford", "ford gran torino"], ["142", "amc", "amc matador"], ["116", "toyota", "toyota carina"], ["285", "peugeot", "peugeot 604sl"], ["102", "chrysler", "chrysler new yorker brougham"], ["131", "toyota", "toyota mark ii"], ["67", "volkswagen", "volkswagen type 3"], ["197", "amc", "amc matador"], ["46", "chevrolet", "chevrolet impala"], ["179", "toyota", "toyota corona"], ["321", "chevrolet", "chevrolet citation"], ["370", "toyota", "toyota cressida"], ["403", "volkswagen", "vw pickup"], ["301", "volkswagen", "vw rabbit custom"], ["187", "volvo", "volvo 244dl"], ["117", "chevrolet", "chevrolet vega"], ["68", "chevrolet", "chevrolet vega"], ["312", "fiat", "fiat strada custom"], ["123", "chevrolet", "chevrolet monte carlo s"], ["314", "chevrolet", "chevrolet citation"], ["229", "chevrolet", "chevrolet caprice classic"], ["317", "volkswagen", "vw rabbit"]]Table model_list: [["ModelId", "Maker", "Model"], ["7", "4", "chevrolet"]]Table car_names: [["MakeId", "Model", "Make"], ["204", "chevrolet", "chevrolet woody"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["3", "bmw", "BMW", "2"]]Table car_names: [["MakeId", "Model", "Make"], ["19", "chevrolet", "chevrolet monte carlo"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"]]Table car_names: [["MakeId", "Model", "Make"], ["152", "toyota", "toyota corona"], ["226", "renault", "renault 5 gtl"], ["375", "chrysler", "chrysler lebaron salon"], ["186", "peugeot", "peugeot 504"], ["244", "ford", "ford mustang ii 2+2"], ["106", "chevrolet", "chevrolet nova custom"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["150", "volkswagen", "volkswagen dasher"]]Table model_list: [["ModelId", "Maker", "Model"], ["32", "21", "volvo"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["5", "ford", "Ford Motor Company", "1"]]Table car_names: [["MakeId", "Model", "Make"], ["141", "chevrolet", "chevrolet chevelle malibu classic"], ["37", "chevrolet", "chevrolet vega 2300"], ["216", "plymouth", "plymouth volare premier v8"], ["38", "toyota", "toyota corona"], ["248", "volkswagen", "volkswagen dasher"], ["263", "ford", "ford fairmont (man)"]]Table model_list: [["ModelId", "Maker", "Model"], ["20", "8", "nissan"]]Table car_names: [["MakeId", "Model", "Make"], ["275", "toyota", "toyota corona"], ["397", "chrysler", "chrysler lebaron medallion"], ["128", "volvo", "volvo 144ea"], ["43", "chevrolet", "chevrolet chevelle malibu"], ["392", "honda", "honda civic"], ["90", "toyota", "toyota corona mark ii (sw)"], ["111", "chevrolet", "chevrolet impala"], ["250", "bmw", "bmw 320i"], ["191", "opel", "opel 1900"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Which are the car makers which produce at least 2 models and more than 3 car makers ? List the id and the maker .	[["2", "volkswagen"], ["4", "gm"], ["5", "ford"], ["6", "chrysler"], ["8", "nissan"], ["19", "toyota"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["362", "renault", "renault 18i"], ["173", "chevrolet", "chevrolet monza 2+2"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["338", "renault", "renault lecar deluxe"], ["334", "volkswagen", "vw dasher (diesel)"], ["333", "volkswagen", "vw rabbit c (diesel)"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["8", "nissan", "Nissan Motors", "4"], ["2", "volkswagen", "Volkswagen", "2"], ["22", "kia", "Kia Motors", "8"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["252", "volkswagen", "volkswagen rabbit custom diesel"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["16", "renault", "Renault", "3"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["241", "volkswagen", "volkswagen rabbit custom"], ["87", "renault", "renault 12 (sw)"], ["200", "chevrolet", "chevrolet nova"], ["180", "volkswagen", "volkswagen dasher"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["319", "chevrolet", "chevrolet chevette"], ["340", " volkswagen", "volkswagen rabbit"], ["194", "renault", "renault 12tl"], ["218", "toyota", "toyota mark ii"], ["283", "volvo", "volvo 264gl"], ["67", "volkswagen", "volkswagen type 3"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["239", "chrysler", "chrysler cordoba"], ["21", "toyota", "toyota corona mark ii"], ["205", "volkswagen", "vw rabbit"], ["376", "chevrolet", "chevrolet cavalier"], ["183", "volkswagen", "volkswagen rabbit"], ["123", "chevrolet", "chevrolet monte carlo s"], ["54", "chevrolet", "chevrolet vega (sw)"], ["245", "chevrolet", "chevrolet chevette"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["26", "16", "renault"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["7", "chevrolet", "chevrolet impala"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["165", "chevrolet", "chevrolet bel air"], ["131", "toyota", "toyota mark ii"], ["211", "volkswagen", "volkswagen rabbit"], ["147", "ford", "ford gran torino (sw)"], ["19", "chevrolet", "chevrolet monte carlo"], ["317", "volkswagen", "vw rabbit"], ["393", "honda", "honda civic (auto)"], ["356", "toyota", "toyota tercel"], ["244", "ford", "ford mustang ii 2+2"], ["301", "volkswagen", "vw rabbit custom"], ["82", "ford", "ford gran torino (sw)"], ["176", "ford", "ford pinto"], ["46", "chevrolet", "chevrolet impala"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["23", "15", "peugeot"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["203", "chevrolet", "chevrolet chevette"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["286", "volkswagen", "volkswagen scirocco"], ["33", "chevrolet", "chevy c20"], ["187", "volvo", "volvo 244dl"], ["360", "ford", "ford escort 2h"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["37", "chevrolet", "chevrolet vega 2300"], ["150", "volkswagen", "volkswagen dasher"], ["221", "chevrolet", "chevy c10"], ["136", "chevrolet", "chevrolet nova"], ["233", "chevrolet", "chevrolet concours"], ["248", "volkswagen", "volkswagen dasher"], ["262", "ford", "ford fairmont (auto)"], ["116", "toyota", "toyota carina"], ["226", "renault", "renault 5 gtl"], ["106", "chevrolet", "chevrolet nova custom"], ["293", "chevrolet", "chevrolet caprice classic"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["90", "toyota", "toyota corona mark ii (sw)"], ["204", "chevrolet", "chevrolet woody"], ["68", "chevrolet", "chevrolet vega"], ["117", "chevrolet", "chevrolet vega"], ["128", "volvo", "volvo 144ea"], ["140", "chevrolet", "chevrolet vega"], ["312", "fiat", "fiat strada custom"], ["285", "peugeot", "peugeot 604sl"], ["78", "chrysler", "chrysler newport royal"], ["229", "chevrolet", "chevrolet caprice classic"], ["179", "toyota", "toyota corona"], ["110", "volkswagen", "volkswagen super beetle"], ["144", "ford", "ford gran torino"], ["38", "toyota", "toyota corona"], ["384", "volkswagen", "volkswagen rabbit l"], ["403", "volkswagen", "vw pickup"], ["370", "toyota", "toyota cressida"], ["152", "toyota", "toyota corona"], ["27", "peugeot", "peugeot 504"], ["275", "toyota", "toyota corona"], ["382", "ford", "ford fairmont futura"], ["111", "chevrolet", "chevrolet impala"], ["335", "audi", "audi 5000s (diesel)"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["3", "bmw", "BMW", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["7", "4", "chevrolet"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["161", "chevrolet", "chevrolet nova"], ["84", "volvo", "volvo 145e (sw)"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["2", "2", "audi"], ["3", "3", "bmw"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["250", "bmw", "bmw 320i"], ["294", "ford", "ford ltd landau"], ["261", "chevrolet", "chevrolet malibu"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["186", "peugeot", "peugeot 504"], ["141", "chevrolet", "chevrolet chevelle malibu classic"], ["369", "volvo", "volvo diesel"], ["337", "honda", "honda civic 1500 gl"], ["122", "fiat", "fiat 124 sport coupe"], ["367", "peugeot", "peugeot 505s turbo diesel"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["32", "21", "volvo"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["276", "datsun", "datsun 510"], ["70", "chevrolet", "chevrolet impala"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["125", "fiat", "fiat 128"], ["353", "honda", "honda civic 1300"], ["24", "ford", "ford maverick"], ["96", "ford", "ford gran torino"], ["198", "ford", "ford gran torino"], ["321", "chevrolet", "chevrolet citation"], ["43", "chevrolet", "chevrolet chevelle malibu"], ["18", "ford", "ford mustang boss 302"], ["40", "volkswagen", "volkswagen super beetle 117"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the ids and makers of all car makers that produce at least 2 models and make more than 3 cars?	[["2", "volkswagen"], ["4", "gm"], ["5", "ford"], ["6", "chrysler"], ["8", "nissan"], ["19", "toyota"]]	2,048	Answer:
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"], ["16", "renault", "Renault", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["12", "9", "fiat"], ["26", "16", "renault"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["13", "daimler benz", "Daimler Benz", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["23", "15", "peugeot"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["8", "nissan", "Nissan Motors", "4"], ["22", "kia", "Kia Motors", "8"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["3", "3", "bmw"], ["31", "2", "volkswagen"], ["20", "8", "nissan"], ["18", "13", "mercedes-benz"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["7", "citroen", "Citroen", "3"], ["4", "gm", "General Motors", "1"], ["3", "bmw", "BMW", "2"]]Table Countries: [["CountryId", "CountryName", "Continent"], ["3", "france", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["15", "11", "honda"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["23", "hyundai", "Hyundai", "8"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["16", "12", "mazda"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["29", "19", "toyota"], ["28", "18", "subaru"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["2", "volkswagen", "Volkswagen", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["22", "14", "opel"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"], ["18", "subaru", "Subaru", "4"]]Table Countries: [["CountryId", "CountryName", "Continent"], ["2", "germany", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["34", "23", "hyundai"], ["7", "4", "chevrolet"], ["32", "21", "volvo"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["11", "honda", "Honda", "4"], ["6", "chrysler", "Chrysler", "1"], ["21", "volvo", "Volvo", "6"]]Table Countries: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["5", "ford", "Ford Motor Company", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["8", "6", "chrysler"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["19", "toyota", "Toyota", "4"]]Table Countries: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"], ["4", "japan", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["25", "4", "pontiac"], ["33", "22", "kia"], ["17", "13", "mercedes"]]Table Countries: [["CountryId", "CountryName", "Continent"], ["5", "italy", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["9", "7", "citroen"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["2", "2", "audi"]]Table Countries: [["CountryId", "CountryName", "Continent"], ["10", "nigeria", "4"], ["14", "mexico", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["10", "8", "datsun"], ["27", "17", "saab"]]Table Countries: [["CountryId", "CountryName", "Continent"], ["8", "korea", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["21", "4", "oldsmobile"], ["35", "6", "jeep"], ["13", "5", "ford"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["24", "6", "plymouth"]]Table Countries: [["CountryId", "CountryName", "Continent"], ["12", "new zealand", "5"], ["11", "australia", "5"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["5", "4", "cadillac"]]Table Countries: [["CountryId", "CountryName", "Continent"], ["7", "uk", "2"], ["6", "sweden", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["4", "4", "buick"]]Table Countries: [["CountryId", "CountryName", "Continent"], ["13", "egypt", "4"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"]]Table Countries: [["CountryId", "CountryName", "Continent"], ["1", "usa", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["11", "6", "dodge"], ["6", "5", "capri"], ["1", "1", "amc"], ["36", "19", "scion"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["19", "5", "mercury"], ["30", "20", "triumph"], ["14", "10", "hi"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the id and names of the countries which have more than 3 car makers or produce the 'fiat' model?	[["1", "usa"], ["2", "germany"], ["4", "japan"], ["5", "italy"]]	2,048	Answer:
Table car_makers: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"]]Table model_list: [["ModelId", "Maker", "Model"], ["12", "9", "fiat"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["16", "renault", "Renault", "3"], ["13", "daimler benz", "Daimler Benz", "2"]]Table model_list: [["ModelId", "Maker", "Model"], ["26", "16", "renault"], ["23", "15", "peugeot"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["8", "nissan", "Nissan Motors", "4"], ["22", "kia", "Kia Motors", "8"]]Table model_list: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"], ["3", "3", "bmw"], ["18", "13", "mercedes-benz"], ["20", "8", "nissan"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"], ["23", "hyundai", "Hyundai", "8"]]Table model_list: [["ModelId", "Maker", "Model"], ["15", "11", "honda"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["7", "citroen", "Citroen", "3"], ["6", "chrysler", "Chrysler", "1"], ["3", "bmw", "BMW", "2"], ["12", "mazda", "Mazda", "4"]]Table model_list: [["ModelId", "Maker", "Model"], ["28", "18", "subaru"], ["16", "12", "mazda"], ["7", "4", "chevrolet"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["2", "volkswagen", "Volkswagen", "2"], ["11", "honda", "Honda", "4"]]Table model_list: [["ModelId", "Maker", "Model"], ["34", "23", "hyundai"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["18", "subaru", "Subaru", "4"]]Table model_list: [["ModelId", "Maker", "Model"], ["29", "19", "toyota"], ["32", "21", "volvo"], ["8", "6", "chrysler"], ["22", "14", "opel"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"], ["5", "ford", "Ford Motor Company", "1"]]Table countries: [["CountryId", "CountryName", "Continent"], ["3", "france", "2"], ["2", "germany", "2"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["21", "volvo", "Volvo", "6"]]Table model_list: [["ModelId", "Maker", "Model"], ["25", "4", "pontiac"], ["33", "22", "kia"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"]]Table countries: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["19", "toyota", "Toyota", "4"]]Table model_list: [["ModelId", "Maker", "Model"], ["17", "13", "mercedes"]]Table countries: [["CountryId", "CountryName", "Continent"], ["4", "japan", "3"], ["9", "russia", "2"]]Table model_list: [["ModelId", "Maker", "Model"], ["10", "8", "datsun"], ["2", "2", "audi"], ["9", "7", "citroen"]]Table countries: [["CountryId", "CountryName", "Continent"], ["5", "italy", "2"], ["10", "nigeria", "4"]]Table model_list: [["ModelId", "Maker", "Model"], ["35", "6", "jeep"]]Table countries: [["CountryId", "CountryName", "Continent"], ["14", "mexico", "1"]]Table model_list: [["ModelId", "Maker", "Model"], ["13", "5", "ford"], ["27", "17", "saab"], ["21", "4", "oldsmobile"]]Table countries: [["CountryId", "CountryName", "Continent"], ["8", "korea", "3"]]Table model_list: [["ModelId", "Maker", "Model"], ["24", "6", "plymouth"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"]]Table model_list: [["ModelId", "Maker", "Model"], ["4", "4", "buick"], ["5", "4", "cadillac"]]Table countries: [["CountryId", "CountryName", "Continent"], ["7", "uk", "2"], ["12", "new zealand", "5"], ["11", "australia", "5"], ["6", "sweden", "2"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["15", "peugeaut", "Peugeaut", "3"]]Table countries: [["CountryId", "CountryName", "Continent"], ["1", "usa", "1"], ["13", "egypt", "4"]]Table model_list: [["ModelId", "Maker", "Model"], ["1", "1", "amc"], ["11", "6", "dodge"], ["6", "5", "capri"], ["36", "19", "scion"]]Table car_makers: [["Id", "Maker", "FullName", "Country"], ["20", "triumph", "Triumph", "7"]]Table model_list: [["ModelId", "Maker", "Model"], ["19", "5", "mercury"], ["30", "20", "triumph"], ["14", "10", "hi"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the ids and names of all countries that either have more than 3 car makers or produce fiat model ?	[["1", "usa"], ["2", "germany"], ["4", "japan"], ["5", "italy"]]	2,048	Answer:
Table AIRLINES: [["uid", "Airline", "Abbreviation", "Country"], ["8", "JetBlue Airways", "JetBlue", "USA"], ["10", "AirTran Airways", "AirTran", "USA"], ["2", "US Airways", "USAir", "USA"], ["1", "United Airlines", "UAL", "USA"], ["3", "Delta Airlines", "Delta", "USA"], ["7", "Continental Airlines", "Continental", "USA"], ["6", "Northwest Airlines", "Northwest", "USA"], ["4", "Southwest Airlines", "Southwest", "USA"], ["9", "Frontier Airlines", "Frontier", "USA"], ["5", "American Airlines", "American", "USA"], ["11", "Allegiant Air", "Allegiant", "USA"], ["12", "Virgin America", "Virgin", "USA"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Which country does Airline "JetBlue Airways" belong to?	[["USA"]]	2,048	Answer:
Table AIRLINES: [["uid", "Airline", "Abbreviation", "Country"], ["8", "JetBlue Airways", "JetBlue", "USA"], ["10", "AirTran Airways", "AirTran", "USA"], ["3", "Delta Airlines", "Delta", "USA"], ["2", "US Airways", "USAir", "USA"], ["1", "United Airlines", "UAL", "USA"], ["7", "Continental Airlines", "Continental", "USA"], ["4", "Southwest Airlines", "Southwest", "USA"], ["9", "Frontier Airlines", "Frontier", "USA"], ["6", "Northwest Airlines", "Northwest", "USA"], ["5", "American Airlines", "American", "USA"], ["11", "Allegiant Air", "Allegiant", "USA"], ["12", "Virgin America", "Virgin", "USA"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What country is Jetblue Airways affiliated with?	[["USA"]]	2,048	Answer:
Table AIRLINES: [["uid", "Airline", "Abbreviation", "Country"], ["8", "JetBlue Airways", "JetBlue", "USA"], ["2", "US Airways", "USAir", "USA"], ["9", "Frontier Airlines", "Frontier", "USA"], ["10", "AirTran Airways", "AirTran", "USA"], ["3", "Delta Airlines", "Delta", "USA"], ["6", "Northwest Airlines", "Northwest", "USA"], ["7", "Continental Airlines", "Continental", "USA"], ["1", "United Airlines", "UAL", "USA"], ["4", "Southwest Airlines", "Southwest", "USA"], ["11", "Allegiant Air", "Allegiant", "USA"], ["5", "American Airlines", "American", "USA"], ["12", "Virgin America", "Virgin", "USA"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the abbreviation of Airline "JetBlue Airways"?	[["JetBlue"]]	2,048	Answer:
Table AIRLINES: [["uid", "Airline", "Abbreviation", "Country"], ["8", "JetBlue Airways", "JetBlue", "USA"], ["2", "US Airways", "USAir", "USA"], ["10", "AirTran Airways", "AirTran", "USA"], ["3", "Delta Airlines", "Delta", "USA"], ["9", "Frontier Airlines", "Frontier", "USA"], ["11", "Allegiant Air", "Allegiant", "USA"], ["7", "Continental Airlines", "Continental", "USA"], ["6", "Northwest Airlines", "Northwest", "USA"], ["1", "United Airlines", "UAL", "USA"], ["4", "Southwest Airlines", "Southwest", "USA"], ["5", "American Airlines", "American", "USA"], ["12", "Virgin America", "Virgin", "USA"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Which abbreviation corresponds to Jetblue Airways?	[["JetBlue"]]	2,048	Answer:
Table AIRLINES: [["uid", "Airline", "Abbreviation", "Country"], ["2", "US Airways", "USAir", "USA"], ["9", "Frontier Airlines", "Frontier", "USA"], ["5", "American Airlines", "American", "USA"], ["1", "United Airlines", "UAL", "USA"], ["7", "Continental Airlines", "Continental", "USA"], ["3", "Delta Airlines", "Delta", "USA"], ["10", "AirTran Airways", "AirTran", "USA"], ["4", "Southwest Airlines", "Southwest", "USA"], ["8", "JetBlue Airways", "JetBlue", "USA"], ["6", "Northwest Airlines", "Northwest", "USA"], ["11", "Allegiant Air", "Allegiant", "USA"], ["12", "Virgin America", "Virgin", "USA"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:List all airline names and their abbreviations in "USA".	[["United Airlines", "UAL"], ["US Airways", "USAir"], ["Delta Airlines", "Delta"], ["Southwest Airlines", "Southwest"], ["American Airlines", "American"], ["Northwest Airlines", "Northwest"], ["Continental Airlines", "Continental"], ["JetBlue Airways", "JetBlue"], ["Frontier Airlines", "Frontier"], ["AirTran Airways", "AirTran"], ["Allegiant Air", "Allegiant"], ["Virgin America", "Virgin"]]	2,048	Answer:
Table AIRLINES: [["uid", "Airline", "Abbreviation", "Country"], ["2", "US Airways", "USAir", "USA"], ["5", "American Airlines", "American", "USA"], ["3", "Delta Airlines", "Delta", "USA"], ["9", "Frontier Airlines", "Frontier", "USA"], ["1", "United Airlines", "UAL", "USA"], ["4", "Southwest Airlines", "Southwest", "USA"], ["7", "Continental Airlines", "Continental", "USA"], ["6", "Northwest Airlines", "Northwest", "USA"], ["8", "JetBlue Airways", "JetBlue", "USA"], ["10", "AirTran Airways", "AirTran", "USA"], ["11", "Allegiant Air", "Allegiant", "USA"], ["12", "Virgin America", "Virgin", "USA"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What are the airline names and abbreviations for airlines in the USA?	[["United Airlines", "UAL"], ["US Airways", "USAir"], ["Delta Airlines", "Delta"], ["Southwest Airlines", "Southwest"], ["American Airlines", "American"], ["Northwest Airlines", "Northwest"], ["Continental Airlines", "Continental"], ["JetBlue Airways", "JetBlue"], ["Frontier Airlines", "Frontier"], ["AirTran Airways", "AirTran"], ["Allegiant Air", "Allegiant"], ["Virgin America", "Virgin"]]	2,048	Answer:
Table AIRPORTS: [["City", "AirportCode", "AirportName", "Country", "CountryAbbrev"], [" Alexandria LA ", "AEX", "Alexandria International ", "United States ", "US"], [" Alexander City AL ", "ALX", "Thomas C Russell Fld ", "United States ", "US"], ["Alexandria Bay ", "AXB", "Alexandria Bay ", "United States ", "US"], ["Albuquerque ", "ABQ", "Albuquerque International ", "United States ", "US"], ["Anthony ", "ANY", "Anthony ", "United States ", "US"], ["Ashland ", "ASX", "Ashland ", "United States ", "US"], ["Aberdeen ", "APG", "Phillips AAF ", "United States ", "US "], ["Alexandria ", "ESF", "Esler Field ", "United States ", "US"], ["Akron CO ", "AKO", "Colorado Plains Regional Airport ", "United States ", "US"], ["Aberdeen ", "ABR", "Municipal ", "United States ", "US"], ["Alexandria ", "AXN", "Alexandria ", "United States ", "US"], ["Ardmore ", "ADM", "Ardmore Municipal Arpt ", "United States ", "US"], ["Alameda ", "NGZ", "NAS ", "United States ", "US"], ["Ardmore ", "AHD", "Downtown ", "United States ", "US"], ["Alitak ", "ALZ", "Alitak SPB ", "United States ", "US"], ["Abingdon ", "VJI", "Virginia Highlands ", "United States ", "US"], ["Alpena ", "APN", "Alpena County Regional ", "United States ", "US"], ["Astoria ", "AST", "Astoria ", "United States ", "US"], ["Afton ", "AFO", "Municipal ", "United States ", "US"], ["Apalachicola ", "AAF", "Municipal ", "United States ", "US"], [" Aliceville AL ", "AIV", "George Downer ", "United States ", "US"], ["Akron/Canton OH ", "CAK", "Akron/canton Regional ", "United States ", "US"], ["Anchorage ", "ANC", "Ted Stevens Anchorage International Airport ", "United States ", "US"], ["Akron/Canton ", "AKC", "Fulton International ", "United States ", "US"], ["Arapahoe ", "AHF", "Municipal ", "United States ", "US"], ["Alamogordo ", "ALM", "Municipal ", "United States ", "US"], ["Albany ", "ALB", "Albany International ", "United States ", "US"], ["Alyeska ", "AQY", "Alyeska ", "United States ", "US"], ["Annapolis ", "ANP", "Lee ", "United States ", "US"], [" Anniston AL ", "ANB", "Anniston Metropolitan ", "United States ", "US"], ["Athens ", "AHN", "Athens ", "United States ", "US"], ["Arlington Heights ", "JLH", "US Army Heliport ", "United States ", "US"], ["Alamogordo ", "HMN", "Holloman AFB ", "United States ", "US"], ["Athens ", "MMI", "McMinn County ", "United States ", "US"], [" Neptune ", "ARX", "Asbury Park ", "United States ", "US"], ["Abilene ", "ABI", "Municipal ", "United States ", "US"], ["Alamosa ", "ALS", "Municipal ", "United States ", "US"], ["Abilene ", "DYS", "Dyess AFB ", "United States ", "US"], ["Albert Lea ", "AEL", "Albert Lea ", "United States ", "US"], ["Altus ", "AXS", "Municipal ", "United States ", "US"], ["Ambler ", "ABL", "Ambler ", "United States ", "US"], ["Alice ", "ALI", "International ", "United States ", "US"], ["Albany ", "CVO", "Albany ", "United States ", "US"], [" Ann Arbor MI ", "ARB", "Municipal ", "United States ", "US"], ["Altus ", "LTS", "Altus AFB ", "United States ", "US"], ["Anaheim ", "ANA", "Orange County Steel Salvage Heliport ", "United States ", "US"], ["Athens ", "ATO", "Ohio University ", "United States ", "US"], ["Albany ", "NAB", "Albany NAS ", "United States ", "US"], ["Apple Valley ", "APV", "Apple Valley ", "United States ", "US"], ["Aiken ", "AIK", "Municipal ", "United States ", "US"], ["Andrews ", "ADR", "Andrews ", "United States ", "US"], ["Adrian ", "ADG", "Lenawee County ", "United States ", "US"], ["Anchorage ", "EDF", "Elmendorf Afb ", "United States ", "US"], ["Anderson ", "AID", "Municipal ", "United States ", "US"], ["Amery ", "AHH", "Municipal ", "United States ", "US"], ["Anniston ", "QAW", "Ft Mcclellan Bus Trml ", "United States ", "US"], ["Anchorage ", "MRI", "Merrill Field ", "United States ", "US"], ["Ainsworth ", "ANW", "Ainsworth ", "United States ", "US"], ["Allakaket ", "AET", "Allakaket ", "United States ", "US"], ["Albany ", "ABY", "Dougherty County ", "United States ", "US"], ["Amchitka ", "AHT", "Amchitka ", "United States ", "US"], ["Amityville ", "AYZ", "Zahns ", "United States ", "US"], ["Adak Island ", "ADK", "Adak Island Ns ", "United States ", "US"], ["Amook ", "AOS", "Amook ", "United States ", "US"], ["Akutan ", "KQA", "Akutan ", "United States ", "US"], ["Anita Bay ", "AIB", "Anita Bay ", "United States ", "US"], ["Anniston ", "RLI", "Reilly AHP ", "United States ", "US"], ["Algona ", "AXG", "Algona ", "United States ", "US"], ["Aleneva ", "AED", "Aleneva ", "United States ", "US"], ["Ada ", "ADT", "Ada ", "United States ", "US"], ["Alton ", "ALN", "Alton ", "United States ", "US"], ["Aniak ", "ANI", "Aniak ", "United States ", "US"], ["Ames ", "AMW", "Ames ", "United States ", "US"], ["Alakanuk ", "AUK", "Alakanuk ", "United States ", "US"], ["Amarillo ", "TDW", "Tradewind ", "United States ", "US"], ["Aleknagik ", "WKK", "Aleknagik ", "United States ", "US"], ["Arcata ", "ACV", "Arcata ", "United States ", "US"], ["Anvik ", "ANV", "Anvik ", "United States ", "US"], ["Anacostia ", "NDV", "USN Heliport ", "United States ", "US"], ["Akiak ", "AKI", "Akiak ", "United States ", "US"], [" Amarillo ", "AMA", "Rick Husband Amarillo International ", "United States ", "US"], ["Akiachak ", "KKI", "Spb ", "United States ", "US"], ["Aspen ", "ASE", "Aspen ", "United States ", "US"], ["Appleton ", "ATW", "Outagamie County ", "United States ", "US"], ["Angola ", "ANQ", "Tri-State Steuben Cty ", "United States ", "US"], ["Anguilla ", "RFK", "Rollang Field ", "United States ", "US"], ["Angoon ", "AGN", "Angoon ", "United States ", "US"], ["Alma ", "AMN", "Gratiot Community ", "United States ", "US"], ["Artesia ", "ATS", "Artesia ", "United States ", "US"], ["Annette Island ", "ANN", "Annette Island ", "United States ", "US"], ["Angel Fire ", "AXX", "Angel Fire ", "United States ", "US"], ["Anderson ", "AND", "Anderson ", "United States ", "US"], ["Anaktuvuk ", "AKP", "Anaktuvuk ", "United States ", "US"], ["Akhiok ", "AKK", "Akhiok SPB ", "United States ", "US"], ["Antlers ", "ATE", "Antlers ", "United States ", "US"], ["Anacortes ", "OTS", "Anacortes ", "United States ", "US"], ["Ashley ", "ASY", "Ashley ", "United States ", "US"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:List the airport code and name in the city of Anthony.	[]	2,048	Answer:
Table AIRPORTS: [["City", "AirportCode", "AirportName", "Country", "CountryAbbrev"], [" Alexandria LA ", "AEX", "Alexandria International ", "United States ", "US"], [" Alexander City AL ", "ALX", "Thomas C Russell Fld ", "United States ", "US"], ["Alexandria Bay ", "AXB", "Alexandria Bay ", "United States ", "US"], ["Ashland ", "ASX", "Ashland ", "United States ", "US"], ["Anthony ", "ANY", "Anthony ", "United States ", "US"], ["Alexandria ", "AXN", "Alexandria ", "United States ", "US"], ["Alexandria ", "ESF", "Esler Field ", "United States ", "US"], ["Albuquerque ", "ABQ", "Albuquerque International ", "United States ", "US"], ["Akron CO ", "AKO", "Colorado Plains Regional Airport ", "United States ", "US"], ["Aberdeen ", "ABR", "Municipal ", "United States ", "US"], ["Ardmore ", "AHD", "Downtown ", "United States ", "US"], ["Ardmore ", "ADM", "Ardmore Municipal Arpt ", "United States ", "US"], ["Aberdeen ", "APG", "Phillips AAF ", "United States ", "US "], ["Akron/Canton OH ", "CAK", "Akron/canton Regional ", "United States ", "US"], ["Astoria ", "AST", "Astoria ", "United States ", "US"], [" Aliceville AL ", "AIV", "George Downer ", "United States ", "US"], ["Akron/Canton ", "AKC", "Fulton International ", "United States ", "US"], ["Afton ", "AFO", "Municipal ", "United States ", "US"], ["Alameda ", "NGZ", "NAS ", "United States ", "US"], ["Athens ", "AHN", "Athens ", "United States ", "US"], ["Albany ", "ALB", "Albany International ", "United States ", "US"], ["Alitak ", "ALZ", "Alitak SPB ", "United States ", "US"], ["Abingdon ", "VJI", "Virginia Highlands ", "United States ", "US"], ["Alpena ", "APN", "Alpena County Regional ", "United States ", "US"], ["Alamogordo ", "ALM", "Municipal ", "United States ", "US"], [" Anniston AL ", "ANB", "Anniston Metropolitan ", "United States ", "US"], ["Apalachicola ", "AAF", "Municipal ", "United States ", "US"], ["Arapahoe ", "AHF", "Municipal ", "United States ", "US"], ["Anchorage ", "ANC", "Ted Stevens Anchorage International Airport ", "United States ", "US"], ["Alyeska ", "AQY", "Alyeska ", "United States ", "US"], ["Athens ", "MMI", "McMinn County ", "United States ", "US"], ["Ambler ", "ABL", "Ambler ", "United States ", "US"], ["Albany ", "CVO", "Albany ", "United States ", "US"], ["Alice ", "ALI", "International ", "United States ", "US"], ["Altus ", "AXS", "Municipal ", "United States ", "US"], [" Neptune ", "ARX", "Asbury Park ", "United States ", "US"], ["Annapolis ", "ANP", "Lee ", "United States ", "US"], ["Arlington Heights ", "JLH", "US Army Heliport ", "United States ", "US"], ["Abilene ", "ABI", "Municipal ", "United States ", "US"], ["Albany ", "NAB", "Albany NAS ", "United States ", "US"], ["Alamogordo ", "HMN", "Holloman AFB ", "United States ", "US"], ["Anniston ", "QAW", "Ft Mcclellan Bus Trml ", "United States ", "US"], ["Albert Lea ", "AEL", "Albert Lea ", "United States ", "US"], ["Athens ", "ATO", "Ohio University ", "United States ", "US"], ["Anderson ", "AID", "Municipal ", "United States ", "US"], ["Aiken ", "AIK", "Municipal ", "United States ", "US"], ["Alamosa ", "ALS", "Municipal ", "United States ", "US"], ["Andrews ", "ADR", "Andrews ", "United States ", "US"], ["Apple Valley ", "APV", "Apple Valley ", "United States ", "US"], ["Abilene ", "DYS", "Dyess AFB ", "United States ", "US"], ["Altus ", "LTS", "Altus AFB ", "United States ", "US"], [" Ann Arbor MI ", "ARB", "Municipal ", "United States ", "US"], ["Anaheim ", "ANA", "Orange County Steel Salvage Heliport ", "United States ", "US"], ["Adrian ", "ADG", "Lenawee County ", "United States ", "US"], ["Amery ", "AHH", "Municipal ", "United States ", "US"], ["Ainsworth ", "ANW", "Ainsworth ", "United States ", "US"], ["Allakaket ", "AET", "Allakaket ", "United States ", "US"], ["Amook ", "AOS", "Amook ", "United States ", "US"], ["Amityville ", "AYZ", "Zahns ", "United States ", "US"], ["Anchorage ", "MRI", "Merrill Field ", "United States ", "US"], ["Algona ", "AXG", "Algona ", "United States ", "US"], ["Anchorage ", "EDF", "Elmendorf Afb ", "United States ", "US"], ["Amchitka ", "AHT", "Amchitka ", "United States ", "US"], ["Aleneva ", "AED", "Aleneva ", "United States ", "US"], ["Albany ", "ABY", "Dougherty County ", "United States ", "US"], ["Adak Island ", "ADK", "Adak Island Ns ", "United States ", "US"], ["Akutan ", "KQA", "Akutan ", "United States ", "US"], ["Ada ", "ADT", "Ada ", "United States ", "US"], ["Alton ", "ALN", "Alton ", "United States ", "US"], ["Ames ", "AMW", "Ames ", "United States ", "US"], ["Anacostia ", "NDV", "USN Heliport ", "United States ", "US"], ["Alakanuk ", "AUK", "Alakanuk ", "United States ", "US"], ["Aleknagik ", "WKK", "Aleknagik ", "United States ", "US"], ["Arcata ", "ACV", "Arcata ", "United States ", "US"], ["Aniak ", "ANI", "Aniak ", "United States ", "US"], ["Akiak ", "AKI", "Akiak ", "United States ", "US"], ["Anniston ", "RLI", "Reilly AHP ", "United States ", "US"], ["Amarillo ", "TDW", "Tradewind ", "United States ", "US"], ["Anvik ", "ANV", "Anvik ", "United States ", "US"], ["Anita Bay ", "AIB", "Anita Bay ", "United States ", "US"], ["Angola ", "ANQ", "Tri-State Steuben Cty ", "United States ", "US"], ["Aspen ", "ASE", "Aspen ", "United States ", "US"], ["Akiachak ", "KKI", "Spb ", "United States ", "US"], ["Anderson ", "AND", "Anderson ", "United States ", "US"], ["Artesia ", "ATS", "Artesia ", "United States ", "US"], ["Appleton ", "ATW", "Outagamie County ", "United States ", "US"], [" Amarillo ", "AMA", "Rick Husband Amarillo International ", "United States ", "US"], ["Alliance ", "AIA", "Alliance ", "United States ", "US"], ["Anguilla ", "RFK", "Rollang Field ", "United States ", "US"], ["Angoon ", "AGN", "Angoon ", "United States ", "US"], ["Alma ", "AMN", "Gratiot Community ", "United States ", "US"], ["Anaktuvuk ", "AKP", "Anaktuvuk ", "United States ", "US"], ["Anacortes ", "OTS", "Anacortes ", "United States ", "US"], ["Annette Island ", "ANN", "Annette Island ", "United States ", "US"], ["Akhiok ", "AKK", "Akhiok SPB ", "United States ", "US"], ["Antlers ", "ATE", "Antlers ", "United States ", "US"], ["Ashley ", "ASY", "Ashley ", "United States ", "US"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Give the airport code and airport name corresonding to the city Anthony.	[]	2,048	Answer:
Table AIRLINES: [["uid", "Airline", "Abbreviation", "Country"], ["2", "US Airways", "USAir", "USA"], ["1", "United Airlines", "UAL", "USA"], ["3", "Delta Airlines", "Delta", "USA"], ["9", "Frontier Airlines", "Frontier", "USA"], ["7", "Continental Airlines", "Continental", "USA"], ["5", "American Airlines", "American", "USA"], ["6", "Northwest Airlines", "Northwest", "USA"], ["4", "Southwest Airlines", "Southwest", "USA"], ["10", "AirTran Airways", "AirTran", "USA"], ["8", "JetBlue Airways", "JetBlue", "USA"], ["11", "Allegiant Air", "Allegiant", "USA"], ["12", "Virgin America", "Virgin", "USA"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:How many airlines do we have?	[["12"]]	2,048	Answer:
Table AIRLINES: [["uid", "Airline", "Abbreviation", "Country"], ["3", "Delta Airlines", "Delta", "USA"], ["7", "Continental Airlines", "Continental", "USA"], ["1", "United Airlines", "UAL", "USA"], ["6", "Northwest Airlines", "Northwest", "USA"], ["10", "AirTran Airways", "AirTran", "USA"], ["9", "Frontier Airlines", "Frontier", "USA"], ["2", "US Airways", "USAir", "USA"], ["5", "American Airlines", "American", "USA"], ["4", "Southwest Airlines", "Southwest", "USA"], ["8", "JetBlue Airways", "JetBlue", "USA"], ["11", "Allegiant Air", "Allegiant", "USA"], ["12", "Virgin America", "Virgin", "USA"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the total number of airlines?	[["12"]]	2,048	Answer:
Table AIRPORTS: [["City", "AirportCode", "AirportName", "Country", "CountryAbbrev"], ["Anchorage ", "EDF", "Elmendorf Afb ", "United States ", "US"], ["Anchorage ", "ANC", "Ted Stevens Anchorage International Airport ", "United States ", "US"], ["Akron CO ", "AKO", "Colorado Plains Regional Airport ", "United States ", "US"], ["Abilene ", "DYS", "Dyess AFB ", "United States ", "US"], ["Alamogordo ", "HMN", "Holloman AFB ", "United States ", "US"], ["Altus ", "LTS", "Altus AFB ", "United States ", "US"], ["Anchorage ", "MRI", "Merrill Field ", "United States ", "US"], ["Aberdeen ", "APG", "Phillips AAF ", "United States ", "US "], ["Alexandria ", "ESF", "Esler Field ", "United States ", "US"], ["Akron/Canton ", "AKC", "Fulton International ", "United States ", "US"], ["Alameda ", "NGZ", "NAS ", "United States ", "US"], ["Anniston ", "RLI", "Reilly AHP ", "United States ", "US"], ["Ambler ", "ABL", "Ambler ", "United States ", "US"], ["Arapahoe ", "AHF", "Municipal ", "United States ", "US"], ["Ardmore ", "AHD", "Downtown ", "United States ", "US"], ["Aleneva ", "AED", "Aleneva ", "United States ", "US"], ["Abingdon ", "VJI", "Virginia Highlands ", "United States ", "US"], ["Anguilla ", "RFK", "Rollang Field ", "United States ", "US"], ["Artesia ", "ATS", "Artesia ", "United States ", "US"], ["Aberdeen ", "ABR", "Municipal ", "United States ", "US"], ["Alexandria Bay ", "AXB", "Alexandria Bay ", "United States ", "US"], ["Allakaket ", "AET", "Allakaket ", "United States ", "US"], ["Arlington Heights ", "JLH", "US Army Heliport ", "United States ", "US"], [" Alexandria LA ", "AEX", "Alexandria International ", "United States ", "US"], ["Angola ", "ANQ", "Tri-State Steuben Cty ", "United States ", "US"], ["Anacostia ", "NDV", "USN Heliport ", "United States ", "US"], [" Anniston AL ", "ANB", "Anniston Metropolitan ", "United States ", "US"], ["Anniston ", "QAW", "Ft Mcclellan Bus Trml ", "United States ", "US"], ["Alitak ", "ALZ", "Alitak SPB ", "United States ", "US"], ["Annapolis ", "ANP", "Lee ", "United States ", "US"], ["Afton ", "AFO", "Municipal ", "United States ", "US"], ["Altus ", "AXS", "Municipal ", "United States ", "US"], ["Adak Island ", "ADK", "Adak Island Ns ", "United States ", "US"], ["Angoon ", "AGN", "Angoon ", "United States ", "US"], ["Alyeska ", "AQY", "Alyeska ", "United States ", "US"], ["Apalachicola ", "AAF", "Municipal ", "United States ", "US"], ["Ashland ", "ASX", "Ashland ", "United States ", "US"], ["Aleknagik ", "WKK", "Aleknagik ", "United States ", "US"], ["Ames ", "AMW", "Ames ", "United States ", "US"], [" Neptune ", "ARX", "Asbury Park ", "United States ", "US"], ["Anvik ", "ANV", "Anvik ", "United States ", "US"], ["Albany ", "ALB", "Albany International ", "United States ", "US"], ["Arctic Village ", "ARC", "Arctic Village ", "United States ", "US"], ["Alice ", "ALI", "International ", "United States ", "US"], ["Ainsworth ", "ANW", "Ainsworth ", "United States ", "US"], ["Anita Bay ", "AIB", "Anita Bay ", "United States ", "US"], [" Alexander City AL ", "ALX", "Thomas C Russell Fld ", "United States ", "US"], ["Alexandria ", "AXN", "Alexandria ", "United States ", "US"], ["Amchitka ", "AHT", "Amchitka ", "United States ", "US"], ["Athens ", "AHN", "Athens ", "United States ", "US"], ["Abilene ", "ABI", "Municipal ", "United States ", "US"], ["Ardmore ", "ADM", "Ardmore Municipal Arpt ", "United States ", "US"], ["Albert Lea ", "AEL", "Albert Lea ", "United States ", "US"], ["Arcata ", "ACV", "Arcata ", "United States ", "US"], ["Alakanuk ", "AUK", "Alakanuk ", "United States ", "US"], ["Akutan ", "KQA", "Akutan ", "United States ", "US"], ["Akiak ", "AKI", "Akiak ", "United States ", "US"], ["Aniak ", "ANI", "Aniak ", "United States ", "US"], ["Amityville ", "AYZ", "Zahns ", "United States ", "US"], ["Albuquerque ", "ABQ", "Albuquerque International ", "United States ", "US"], ["Astoria ", "AST", "Astoria ", "United States ", "US"], ["Amook ", "AOS", "Amook ", "United States ", "US"], ["Anaktuvuk ", "AKP", "Anaktuvuk ", "United States ", "US"], ["Albany ", "NAB", "Albany NAS ", "United States ", "US"], ["Algona ", "AXG", "Algona ", "United States ", "US"], ["Akron/Canton OH ", "CAK", "Akron/canton Regional ", "United States ", "US"], ["Athens ", "MMI", "McMinn County ", "United States ", "US"], ["Annette Island ", "ANN", "Annette Island ", "United States ", "US"], ["Anderson ", "AID", "Municipal ", "United States ", "US"], ["Adrian ", "ADG", "Lenawee County ", "United States ", "US"], ["Alpena ", "APN", "Alpena County Regional ", "United States ", "US"], ["Alamosa ", "ALS", "Municipal ", "United States ", "US"], [" Aliceville AL ", "AIV", "George Downer ", "United States ", "US"], ["Athens ", "ATO", "Ohio University ", "United States ", "US"], ["Alamogordo ", "ALM", "Municipal ", "United States ", "US"], ["Albany ", "CVO", "Albany ", "United States ", "US"], ["Amarillo ", "TDW", "Tradewind ", "United States ", "US"], ["Alma ", "AMN", "Gratiot Community ", "United States ", "US"], ["Aspen ", "ASE", "Aspen ", "United States ", "US"], ["Akiachak ", "KKI", "Spb ", "United States ", "US"], ["Aiken ", "AIK", "Municipal ", "United States ", "US"], ["Andrews ", "ADR", "Andrews ", "United States ", "US"], ["Amery ", "AHH", "Municipal ", "United States ", "US"], ["Anacortes ", "OTS", "Anacortes ", "United States ", "US"], ["Alton ", "ALN", "Alton ", "United States ", "US"], [" Ann Arbor MI ", "ARB", "Municipal ", "United States ", "US"], ["Akhiok ", "AKK", "Akhiok SPB ", "United States ", "US"], ["Angel Fire ", "AXX", "Angel Fire ", "United States ", "US"], ["Apple Valley ", "APV", "Apple Valley ", "United States ", "US"], ["Alliance ", "AIA", "Alliance ", "United States ", "US"], ["Alpine ", "ALE", "Alpine ", "United States ", "US"], ["Anaheim ", "ANA", "Orange County Steel Salvage Heliport ", "United States ", "US"], [" Amarillo ", "AMA", "Rick Husband Amarillo International ", "United States ", "US"], ["Albany ", "ABY", "Dougherty County ", "United States ", "US"], ["Ada ", "ADT", "Ada ", "United States ", "US"], ["Appleton ", "ATW", "Outagamie County ", "United States ", "US"], ["Anderson ", "AND", "Anderson ", "United States ", "US"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:How many airports do we have?	[["100"]]	2,048	Answer:
Table AIRPORTS: [["City", "AirportCode", "AirportName", "Country", "CountryAbbrev"], ["Akron CO ", "AKO", "Colorado Plains Regional Airport ", "United States ", "US"], ["Anchorage ", "EDF", "Elmendorf Afb ", "United States ", "US"], ["Abilene ", "DYS", "Dyess AFB ", "United States ", "US"], ["Anchorage ", "ANC", "Ted Stevens Anchorage International Airport ", "United States ", "US"], ["Aberdeen ", "APG", "Phillips AAF ", "United States ", "US "], ["Alamogordo ", "HMN", "Holloman AFB ", "United States ", "US"], ["Altus ", "LTS", "Altus AFB ", "United States ", "US"], ["Alexandria ", "ESF", "Esler Field ", "United States ", "US"], ["Anchorage ", "MRI", "Merrill Field ", "United States ", "US"], ["Akron/Canton ", "AKC", "Fulton International ", "United States ", "US"], ["Alameda ", "NGZ", "NAS ", "United States ", "US"], ["Abingdon ", "VJI", "Virginia Highlands ", "United States ", "US"], ["Ambler ", "ABL", "Ambler ", "United States ", "US"], ["Aberdeen ", "ABR", "Municipal ", "United States ", "US"], ["Ashland ", "ASX", "Ashland ", "United States ", "US"], [" Alexandria LA ", "AEX", "Alexandria International ", "United States ", "US"], ["Alitak ", "ALZ", "Alitak SPB ", "United States ", "US"], ["Alexandria Bay ", "AXB", "Alexandria Bay ", "United States ", "US"], ["Allakaket ", "AET", "Allakaket ", "United States ", "US"], ["Albuquerque ", "ABQ", "Albuquerque International ", "United States ", "US"], ["Ainsworth ", "ANW", "Ainsworth ", "United States ", "US"], ["Aleneva ", "AED", "Aleneva ", "United States ", "US"], ["Ardmore ", "AHD", "Downtown ", "United States ", "US"], ["Arapahoe ", "AHF", "Municipal ", "United States ", "US"], ["Artesia ", "ATS", "Artesia ", "United States ", "US"], ["Anita Bay ", "AIB", "Anita Bay ", "United States ", "US"], ["Anniston ", "RLI", "Reilly AHP ", "United States ", "US"], ["Alyeska ", "AQY", "Alyeska ", "United States ", "US"], ["Ames ", "AMW", "Ames ", "United States ", "US"], ["Adak Island ", "ADK", "Adak Island Ns ", "United States ", "US"], ["Amchitka ", "AHT", "Amchitka ", "United States ", "US"], ["Arlington Heights ", "JLH", "US Army Heliport ", "United States ", "US"], ["Anniston ", "QAW", "Ft Mcclellan Bus Trml ", "United States ", "US"], ["Akron/Canton OH ", "CAK", "Akron/canton Regional ", "United States ", "US"], ["Abilene ", "ABI", "Municipal ", "United States ", "US"], ["Anguilla ", "RFK", "Rollang Field ", "United States ", "US"], [" Neptune ", "ARX", "Asbury Park ", "United States ", "US"], ["Annapolis ", "ANP", "Lee ", "United States ", "US"], ["Albert Lea ", "AEL", "Albert Lea ", "United States ", "US"], ["Albany ", "ALB", "Albany International ", "United States ", "US"], ["Astoria ", "AST", "Astoria ", "United States ", "US"], ["Anvik ", "ANV", "Anvik ", "United States ", "US"], ["Angoon ", "AGN", "Angoon ", "United States ", "US"], ["Athens ", "AHN", "Athens ", "United States ", "US"], ["Amook ", "AOS", "Amook ", "United States ", "US"], ["Angola ", "ANQ", "Tri-State Steuben Cty ", "United States ", "US"], ["Altus ", "AXS", "Municipal ", "United States ", "US"], ["Aleknagik ", "WKK", "Aleknagik ", "United States ", "US"], ["Akiak ", "AKI", "Akiak ", "United States ", "US"], ["Alakanuk ", "AUK", "Alakanuk ", "United States ", "US"], ["Alice ", "ALI", "International ", "United States ", "US"], ["Akutan ", "KQA", "Akutan ", "United States ", "US"], [" Alexander City AL ", "ALX", "Thomas C Russell Fld ", "United States ", "US"], [" Anniston AL ", "ANB", "Anniston Metropolitan ", "United States ", "US"], ["Aniak ", "ANI", "Aniak ", "United States ", "US"], ["Alexandria ", "AXN", "Alexandria ", "United States ", "US"], ["Arcata ", "ACV", "Arcata ", "United States ", "US"], ["Aspen ", "ASE", "Aspen ", "United States ", "US"], ["Afton ", "AFO", "Municipal ", "United States ", "US"], ["Apalachicola ", "AAF", "Municipal ", "United States ", "US"], ["Anacostia ", "NDV", "USN Heliport ", "United States ", "US"], ["Amityville ", "AYZ", "Zahns ", "United States ", "US"], ["Algona ", "AXG", "Algona ", "United States ", "US"], ["Ardmore ", "ADM", "Ardmore Municipal Arpt ", "United States ", "US"], ["Arctic Village ", "ARC", "Arctic Village ", "United States ", "US"], ["Athens ", "MMI", "McMinn County ", "United States ", "US"], ["Amarillo ", "TDW", "Tradewind ", "United States ", "US"], ["Albany ", "NAB", "Albany NAS ", "United States ", "US"], ["Alton ", "ALN", "Alton ", "United States ", "US"], ["Albany ", "CVO", "Albany ", "United States ", "US"], ["Akhiok ", "AKK", "Akhiok SPB ", "United States ", "US"], ["Ada ", "ADT", "Ada ", "United States ", "US"], ["Alamogordo ", "ALM", "Municipal ", "United States ", "US"], ["Apple Valley ", "APV", "Apple Valley ", "United States ", "US"], ["Akiachak ", "KKI", "Spb ", "United States ", "US"], ["Angel Fire ", "AXX", "Angel Fire ", "United States ", "US"], ["Aiken ", "AIK", "Municipal ", "United States ", "US"], ["Andrews ", "ADR", "Andrews ", "United States ", "US"], [" Ann Arbor MI ", "ARB", "Municipal ", "United States ", "US"], ["Alpena ", "APN", "Alpena County Regional ", "United States ", "US"], ["Adrian ", "ADG", "Lenawee County ", "United States ", "US"], ["Anderson ", "AID", "Municipal ", "United States ", "US"], ["Alamosa ", "ALS", "Municipal ", "United States ", "US"], ["Annette Island ", "ANN", "Annette Island ", "United States ", "US"], [" Aliceville AL ", "AIV", "George Downer ", "United States ", "US"], ["Anaktuvuk ", "AKP", "Anaktuvuk ", "United States ", "US"], ["Anacortes ", "OTS", "Anacortes ", "United States ", "US"], ["Athens ", "ATO", "Ohio University ", "United States ", "US"], ["Amery ", "AHH", "Municipal ", "United States ", "US"], ["Albany ", "ABY", "Dougherty County ", "United States ", "US"], ["Anaheim ", "ANA", "Orange County Steel Salvage Heliport ", "United States ", "US"], ["Appleton ", "ATW", "Outagamie County ", "United States ", "US"], ["Ashley ", "ASY", "Ashley ", "United States ", "US"], ["Alpine ", "ALE", "Alpine ", "United States ", "US"], ["Alma ", "AMN", "Gratiot Community ", "United States ", "US"], ["Alliance ", "AIA", "Alliance ", "United States ", "US"], ["Anderson ", "AND", "Anderson ", "United States ", "US"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Return the number of airports.	[["100"]]	2,048	Answer:
Table FLIGHTS: [["Airline", "FlightNo", "SourceAirport", "DestAirport"], ["2", "747", " AST", " ANC"], ["8", "1419", " ATS", " AEL"], ["7", "250", " AXS", " ATS"], ["8", "1418", " AEL", " ATS"], ["9", "747", " ESF", " WKK"], ["2", "1076", " AXG", " ABQ"], ["1", "143", " AKC", " ALI"], ["1", "1167", " AAF", " ABQ"], ["1", "1329", " LTS", " RLI"], ["2", "1077", " ABQ", " AXG"], ["6", "162", " NDV", " AOS"], ["1", "744", " AED", " OTS"], ["1", "1166", " ABQ", " AAF"], ["1", "1328", " RLI", " LTS"], ["1", "142", " ALI", " AKC"], ["2", "384", " AOS", " AGN"], ["12", "766", " RLI", " AND"], ["6", "210", " AXG", " AQY"], ["1", "561", " ATO", " AHF"], ["8", "640", " ABQ", " AOS"], ["1", "148", " HMN", " ABL"], ["7", "813", " AXN", " AND"], ["8", "1347", " ESF", " AND"], ["10", "175", " AMA", " AED"], ["1", "425", " AOS", " ADG"], ["4", "564", " AYZ", " AED"], ["10", "1298", " ATS", " ALS"], ["7", "812", " AND", " AXN"], ["10", "1299", " ALS", " ATS"], ["8", "1086", " EDF", " AFO"], ["3", "334", " ALI", " ATW"], ["1", "199", " AAF", " NGZ"], ["3", "285", " AXN", " AXB"], ["8", "737", " ABQ", " ACV"], ["12", "767", " AND", " RLI"], ["6", "179", " ALI", " ABQ"], ["7", "571", " ADG", " OTS"], ["1", "1062", " AQY", " AFO"], ["3", "335", " ATW", " ALI"], ["9", "748", " ATS", " RLI"], ["7", "737", " ABY", " ARX"], ["11", "436", " ABQ", " AND"], ["10", "737", " AMN", " QAW"], ["7", "251", " ATS", " AXS"], ["1", "1063", " AFO", " AQY"], ["7", "1031", " AXS", " AED"], ["8", "641", " AOS", " ABQ"], ["12", "326", " ATO", " AOS"], ["1", "198", " NGZ", " AAF"], ["3", "1041", " OTS", " AHF"], ["1", "560", " AHF", " ATO"], ["1", "745", " OTS", " AED"], ["1", "149", " ABL", " HMN"], ["7", "1081", " AGN", " AHF"], ["7", "1071", " ARB", " AET"], ["7", "663", " AKO", " ATS"], ["6", "178", " ABQ", " ALI"], ["1", "829", " EDF", " HMN"], ["3", "460", " RLI", " AHD"], ["4", "565", " AED", " AYZ"], ["1", "828", " HMN", " EDF"], ["7", "337", " AUK", " AXG"], ["8", "175", " ABQ", " DYS"], ["2", "1286", " HMN", " ESF"], ["11", "437", " AND", " ABQ"], ["3", "826", " ABQ", " AEL"], ["7", "336", " AXG", " AUK"], ["12", "891", " ALS", " ATO"], ["8", "174", " DYS", " ABQ"], ["2", "193", " NDV", " AKK"], ["6", "252", " AXG", " ANY"], ["3", "1187", " AET", " ANB"], ["2", "466", " ARB", " AGN"], ["2", "693", " ATS", " CAK"], ["1", "1027", " DYS", " AHN"], ["3", "1186", " ANB", " AET"], ["9", "749", " RLI", " ATS"], ["1", "996", " AXG", " AXS"], ["8", "1346", " AND", " ESF"], ["2", "1243", " AMA", " AED"], ["1", "626", " AKI", " AEL"], ["6", "250", " AXX", " AED"], ["3", "827", " AEL", " ABQ"], ["12", "327", " AOS", " ATO"], ["2", "385", " AGN", " AOS"], ["1", "1026", " AHN", " DYS"], ["7", "721", " KQA", " AED"], ["8", "372", " AHF", " AKC"], ["10", "600", " ABQ", " AKI"], ["7", "662", " ATS", " AKO"], ["3", "862", " LTS", " RLI"], ["10", "174", " AED", " AMA"], ["1", "424", " ADG", " AOS"], ["9", "159", " AQY", " AKO"], ["4", "265", " ATW", " ARB"], ["2", "692", " CAK", " ATS"], ["8", "373", " AKC", " AHF"], ["2", "746", " ANC", " AST"], ["7", "69", " AFO", " EDF"], ["1", "277", " ANV", " ARB"], ["1", "276", " ARB", " ANV"], ["1", "470", " ABQ", " MMI"], ["2", "1242", " AED", " AMA"], ["2", "322", " ALN", " ARB"], ["4", "1162", " ANW", " AKO"], ["7", "118", " AKK", " ANW"], ["2", "330", " AYZ", " AND"], ["2", "467", " AGN", " ARB"], ["2", "1377", " APG", " ALI"], ["2", "1375", " AMW", " ALI"], ["6", "708", " ANB", " ALI"], ["1", "1251", " AED", " ARC"], ["6", "671", " HMN", " AYZ"], ["6", "211", " AQY", " AXG"], ["7", "1030", " AED", " AXS"], ["4", "167", " AKI", " AAF"], ["1", "627", " AEL", " AKI"], ["2", "143", " ADM", " ABQ"], ["1", "997", " AXS", " AXG"], ["4", "169", " ANY", " ALI"], ["3", "245", " AHT", " EDF"], ["1", "1250", " ARC", " AED"], ["3", "396", " AED", " ASX"], ["6", "741", " AXG", " HMN"], ["8", "1281", " AET", " ATO"], ["7", "68", " EDF", " AFO"], ["9", "225", " AHF", " ASY"], ["3", "85", " ALS", " ABY"], ["6", "971", " AOS", " ASE"], ["10", "7", " AXN", " TDW"], ["7", "570", " OTS", " ADG"], ["4", "168", " ALI", " ANY"], ["11", "591", " AAF", " ANY"], ["1", "108", " ANV", " MMI"], ["8", "1349", " AHF", " ANI"], ["11", "566", " LTS", " AET"], ["7", "792", " AIA", " NGZ"], ["3", "195", " ARX", " AST"], ["5", "350", " ATW", " AOS"], ["7", "188", " AND", " ALZ"], ["7", "119", " ANW", " AKK"], ["3", "1094", " AAF", " ALS"], ["1", "326", " ALX", " ALI"], ["7", "720", " AED", " KQA"], ["4", "1036", " ANQ", " ALS"], ["2", "903", " OTS", " AKO"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:How many flights do we have?	[["1200"]]	2,048	Answer:
Table FLIGHTS: [["Airline", "FlightNo", "SourceAirport", "DestAirport"], ["2", "747", " AST", " ANC"], ["2", "1076", " AXG", " ABQ"], ["8", "1419", " ATS", " AEL"], ["2", "1077", " ABQ", " AXG"], ["8", "1418", " AEL", " ATS"], ["11", "436", " ABQ", " AND"], ["11", "437", " AND", " ABQ"], ["7", "250", " AXS", " ATS"], ["8", "640", " ABQ", " AOS"], ["1", "470", " ABQ", " MMI"], ["3", "460", " RLI", " AHD"], ["1", "1167", " AAF", " ABQ"], ["6", "210", " AXG", " AQY"], ["8", "175", " ABQ", " DYS"], ["9", "748", " ATS", " RLI"], ["6", "179", " ALI", " ABQ"], ["7", "737", " ABY", " ARX"], ["7", "251", " ATS", " AXS"], ["3", "827", " AEL", " ABQ"], ["3", "826", " ABQ", " AEL"], ["7", "722", " AST", " CAK"], ["8", "641", " AOS", " ABQ"], ["7", "571", " ADG", " OTS"], ["1", "471", " MMI", " ABQ"], ["8", "752", " AEL", " ATE"], ["12", "121", " ADK", " ASY"], ["6", "211", " AQY", " AXG"], ["1", "1166", " ABQ", " AAF"], ["2", "693", " ATS", " CAK"], ["7", "316", " AST", " AKI"], ["9", "749", " RLI", " ATS"], ["2", "692", " CAK", " ATS"], ["9", "279", " AST", " AHH"], ["10", "600", " ABQ", " AKI"], ["7", "721", " KQA", " AED"], ["8", "174", " DYS", " ABQ"], ["7", "720", " AED", " KQA"], ["7", "1081", " AGN", " AHF"], ["9", "230", " ANY", " ABQ"], ["3", "461", " AHD", " RLI"], ["7", "1071", " ARB", " AET"], ["1", "1329", " LTS", " RLI"], ["6", "162", " NDV", " AOS"], ["1", "745", " OTS", " AED"], ["2", "466", " ARB", " AGN"], ["9", "225", " AHF", " ASY"], ["7", "723", " CAK", " AST"], ["7", "370", " QAW", " ATW"], ["1", "425", " AOS", " ADG"], ["1", "561", " ATO", " AHF"], ["7", "371", " ATW", " QAW"], ["8", "513", " ADK", " ABQ"], ["6", "178", " ABQ", " ALI"], ["2", "1361", " AST", " AHH"], ["1", "1328", " RLI", " LTS"], ["12", "425", " ABY", " RLI"], ["8", "671", " CVO", " ASY"], ["1", "143", " AKC", " ALI"], ["12", "766", " RLI", " AND"], ["12", "1127", " ABY", " RLI"], ["2", "467", " AGN", " ARB"], ["12", "326", " ATO", " AOS"], ["10", "601", " AKI", " ABQ"], ["6", "372", " ATE", " MMI"], ["1", "91", " ABR", " ATO"], ["1", "309", " AKC", " ATE"], ["4", "871", " ARB", " ATE"], ["6", "373", " MMI", " ATE"], ["9", "942", " ABI", " MMI"], ["11", "470", " CAK", " AHT"], ["12", "767", " AND", " RLI"], ["1", "1027", " DYS", " AHN"], ["7", "115", " ADK", " ARB"], ["6", "671", " HMN", " AYZ"], ["1", "744", " AED", " OTS"], ["3", "195", " ARX", " AST"], ["2", "143", " ADM", " ABQ"], ["1", "626", " AKI", " AEL"], ["3", "1186", " ANB", " AET"], ["3", "334", " ALI", " ATW"], ["6", "670", " AYZ", " HMN"], ["2", "385", " AGN", " AOS"], ["1", "148", " HMN", " ABL"], ["2", "193", " NDV", " AKK"], ["1", "149", " ABL", " HMN"], ["8", "753", " ATE", " AEL"], ["7", "317", " AKI", " AST"], ["3", "1187", " AET", " ANB"], ["10", "737", " AMN", " QAW"], ["12", "1126", " RLI", " ABY"], ["6", "1071", " ABR", " ADK"], ["11", "285", " WKK", " ABQ"], ["12", "327", " AOS", " ATO"], ["11", "908", " ANQ", " ATO"], ["2", "670", " KKI", " ATS"], ["9", "158", " AKO", " AQY"], ["7", "754", " ABR", " MMI"], ["6", "252", " AXG", " ANY"], ["5", "1205", " AST", " AOS"], ["8", "670", " ASY", " CVO"], ["7", "455", " ADK", " KQA"], ["7", "812", " AND", " AXN"], ["12", "651", " AXN", " CAK"], ["7", "570", " OTS", " ADG"], ["1", "90", " ATO", " ABR"], ["2", "384", " AOS", " AGN"], ["8", "737", " ABQ", " ACV"], ["3", "335", " ATW", " ALI"], ["7", "755", " MMI", " ABR"], ["7", "1031", " AXS", " AED"], ["8", "872", " ABY", " CAK"], ["7", "663", " AKO", " ATS"], ["11", "909", " ATO", " ANQ"], ["1", "627", " AEL", " AKI"], ["9", "747", " ESF", " WKK"], ["9", "224", " ASY", " AHF"], ["1", "560", " AHF", " ATO"], ["7", "561", " ASE", " ATW"], ["7", "813", " AXN", " AND"], ["8", "542", " ABQ", " NGZ"], ["1", "308", " ATE", " AKC"], ["9", "231", " ABQ", " ANY"], ["6", "250", " AXX", " AED"], ["7", "45", " ABR", " AKI"], ["8", "372", " AHF", " AKC"], ["4", "565", " AED", " AYZ"], ["12", "891", " ALS", " ATO"], ["3", "245", " AHT", " EDF"], ["9", "1114", " LTS", " ABI"], ["10", "1137", " ABI", " ABL"], ["12", "450", " ALB", " HMN"], ["8", "512", " ABQ", " ADK"], ["5", "1204", " AOS", " AST"], ["9", "159", " AQY", " AKO"], ["7", "662", " ATS", " AKO"], ["3", "285", " AXN", " AXB"], ["7", "792", " AIA", " NGZ"], ["2", "746", " ANC", " AST"], ["1", "1026", " AHN", " DYS"], ["8", "1281", " AET", " ATO"], ["11", "135", " ADG", " NDV"], ["10", "1391", " ABL", " ADG"], ["1", "108", " ANV", " MMI"], ["10", "1136", " ABL", " ABI"], ["11", "471", " AHT", " CAK"], ["7", "77", " ABL", " ARB"], ["6", "621", " OTS", " AST"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Return the number of flights.	[["1200"]]	2,048	Answer:
Table AIRLINES: [["uid", "Airline", "Abbreviation", "Country"], ["1", "United Airlines", "UAL", "USA"], ["9", "Frontier Airlines", "Frontier", "USA"], ["2", "US Airways", "USAir", "USA"], ["3", "Delta Airlines", "Delta", "USA"], ["4", "Southwest Airlines", "Southwest", "USA"], ["7", "Continental Airlines", "Continental", "USA"], ["6", "Northwest Airlines", "Northwest", "USA"], ["10", "AirTran Airways", "AirTran", "USA"], ["5", "American Airlines", "American", "USA"], ["8", "JetBlue Airways", "JetBlue", "USA"], ["11", "Allegiant Air", "Allegiant", "USA"], ["12", "Virgin America", "Virgin", "USA"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Which airline has abbreviation 'UAL'?	[["United Airlines"]]	2,048	Answer:
Table AIRLINES: [["uid", "Airline", "Abbreviation", "Country"], ["1", "United Airlines", "UAL", "USA"], ["9", "Frontier Airlines", "Frontier", "USA"], ["2", "US Airways", "USAir", "USA"], ["3", "Delta Airlines", "Delta", "USA"], ["7", "Continental Airlines", "Continental", "USA"], ["4", "Southwest Airlines", "Southwest", "USA"], ["5", "American Airlines", "American", "USA"], ["10", "AirTran Airways", "AirTran", "USA"], ["6", "Northwest Airlines", "Northwest", "USA"], ["11", "Allegiant Air", "Allegiant", "USA"], ["8", "JetBlue Airways", "JetBlue", "USA"], ["12", "Virgin America", "Virgin", "USA"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Give the airline with abbreviation 'UAL'.	[["United Airlines"]]	2,048	Answer:
Table AIRLINES: [["uid", "Airline", "Abbreviation", "Country"], ["2", "US Airways", "USAir", "USA"], ["1", "United Airlines", "UAL", "USA"], ["5", "American Airlines", "American", "USA"], ["3", "Delta Airlines", "Delta", "USA"], ["7", "Continental Airlines", "Continental", "USA"], ["9", "Frontier Airlines", "Frontier", "USA"], ["6", "Northwest Airlines", "Northwest", "USA"], ["4", "Southwest Airlines", "Southwest", "USA"], ["10", "AirTran Airways", "AirTran", "USA"], ["8", "JetBlue Airways", "JetBlue", "USA"], ["11", "Allegiant Air", "Allegiant", "USA"], ["12", "Virgin America", "Virgin", "USA"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:How many airlines are from USA?	[["12"]]	2,048	Answer:
Table AIRLINES: [["uid", "Airline", "Abbreviation", "Country"], ["2", "US Airways", "USAir", "USA"], ["5", "American Airlines", "American", "USA"], ["9", "Frontier Airlines", "Frontier", "USA"], ["6", "Northwest Airlines", "Northwest", "USA"], ["7", "Continental Airlines", "Continental", "USA"], ["3", "Delta Airlines", "Delta", "USA"], ["1", "United Airlines", "UAL", "USA"], ["10", "AirTran Airways", "AirTran", "USA"], ["4", "Southwest Airlines", "Southwest", "USA"], ["8", "JetBlue Airways", "JetBlue", "USA"], ["11", "Allegiant Air", "Allegiant", "USA"], ["12", "Virgin America", "Virgin", "USA"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Return the number of airlines in the USA.	[["12"]]	2,048	Answer:
Table AIRPORTS: [["City", "AirportCode", "AirportName", "Country", "CountryAbbrev"], ["Alton ", "ALN", "Alton ", "United States ", "US"], ["Abingdon ", "VJI", "Virginia Highlands ", "United States ", "US"], ["Akron CO ", "AKO", "Colorado Plains Regional Airport ", "United States ", "US"], ["Anchorage ", "ANC", "Ted Stevens Anchorage International Airport ", "United States ", "US"], ["Anchorage ", "EDF", "Elmendorf Afb ", "United States ", "US"], ["Abilene ", "DYS", "Dyess AFB ", "United States ", "US"], ["Alexandria ", "ESF", "Esler Field ", "United States ", "US"], ["Alamogordo ", "HMN", "Holloman AFB ", "United States ", "US"], ["Anchorage ", "MRI", "Merrill Field ", "United States ", "US"], ["Aberdeen ", "APG", "Phillips AAF ", "United States ", "US "], ["Akron/Canton ", "AKC", "Fulton International ", "United States ", "US"], [" Alexandria LA ", "AEX", "Alexandria International ", "United States ", "US"], ["Anniston ", "RLI", "Reilly AHP ", "United States ", "US"], ["Arapahoe ", "AHF", "Municipal ", "United States ", "US"], ["Aberdeen ", "ABR", "Municipal ", "United States ", "US"], ["Amchitka ", "AHT", "Amchitka ", "United States ", "US"], ["Athens ", "MMI", "McMinn County ", "United States ", "US"], [" Alexander City AL ", "ALX", "Thomas C Russell Fld ", "United States ", "US"], ["Anaheim ", "ANA", "Orange County Steel Salvage Heliport ", "United States ", "US"], ["Adrian ", "ADG", "Lenawee County ", "United States ", "US"], ["Albany ", "ABY", "Dougherty County ", "United States ", "US"], ["Anniston ", "QAW", "Ft Mcclellan Bus Trml ", "United States ", "US"], ["Angola ", "ANQ", "Tri-State Steuben Cty ", "United States ", "US"], [" Anniston AL ", "ANB", "Anniston Metropolitan ", "United States ", "US"], ["Alyeska ", "AQY", "Alyeska ", "United States ", "US"], ["Ashland ", "ASX", "Ashland ", "United States ", "US"], ["Alexandria Bay ", "AXB", "Alexandria Bay ", "United States ", "US"], ["Ardmore ", "AHD", "Downtown ", "United States ", "US"], [" Aliceville AL ", "AIV", "George Downer ", "United States ", "US"], ["Abilene ", "ABI", "Municipal ", "United States ", "US"], ["Alpena ", "APN", "Alpena County Regional ", "United States ", "US"], ["Arlington Heights ", "JLH", "US Army Heliport ", "United States ", "US"], ["Amityville ", "AYZ", "Zahns ", "United States ", "US"], ["Afton ", "AFO", "Municipal ", "United States ", "US"], ["Altus ", "LTS", "Altus AFB ", "United States ", "US"], [" Neptune ", "ARX", "Asbury Park ", "United States ", "US"], ["Albany ", "ALB", "Albany International ", "United States ", "US"], ["Albuquerque ", "ABQ", "Albuquerque International ", "United States ", "US"], ["Ames ", "AMW", "Ames ", "United States ", "US"], ["Amery ", "AHH", "Municipal ", "United States ", "US"], ["Anguilla ", "RFK", "Rollang Field ", "United States ", "US"], ["Aleneva ", "AED", "Aleneva ", "United States ", "US"], ["Apalachicola ", "AAF", "Municipal ", "United States ", "US"], ["Alamogordo ", "ALM", "Municipal ", "United States ", "US"], ["Aleknagik ", "WKK", "Aleknagik ", "United States ", "US"], ["Alakanuk ", "AUK", "Alakanuk ", "United States ", "US"], ["Altus ", "AXS", "Municipal ", "United States ", "US"], ["Adak Island ", "ADK", "Adak Island Ns ", "United States ", "US"], ["Ambler ", "ABL", "Ambler ", "United States ", "US"], ["Amarillo ", "TDW", "Tradewind ", "United States ", "US"], ["Allakaket ", "AET", "Allakaket ", "United States ", "US"], ["Appleton ", "ATW", "Outagamie County ", "United States ", "US"], ["Artesia ", "ATS", "Artesia ", "United States ", "US"], ["Alexandria ", "AXN", "Alexandria ", "United States ", "US"], ["Amook ", "AOS", "Amook ", "United States ", "US"], ["Annapolis ", "ANP", "Lee ", "United States ", "US"], ["Alameda ", "NGZ", "NAS ", "United States ", "US"], ["Akron/Canton OH ", "CAK", "Akron/canton Regional ", "United States ", "US"], ["Anacostia ", "NDV", "USN Heliport ", "United States ", "US"], ["Alitak ", "ALZ", "Alitak SPB ", "United States ", "US"], ["Anvik ", "ANV", "Anvik ", "United States ", "US"], ["Annette Island ", "ANN", "Annette Island ", "United States ", "US"], ["Athens ", "AHN", "Athens ", "United States ", "US"], ["Aiken ", "AIK", "Municipal ", "United States ", "US"], ["Angoon ", "AGN", "Angoon ", "United States ", "US"], ["Ainsworth ", "ANW", "Ainsworth ", "United States ", "US"], ["Andrews ", "ADR", "Andrews ", "United States ", "US"], ["Anderson ", "AID", "Municipal ", "United States ", "US"], [" Amarillo ", "AMA", "Rick Husband Amarillo International ", "United States ", "US"], ["Albert Lea ", "AEL", "Albert Lea ", "United States ", "US"], [" Ann Arbor MI ", "ARB", "Municipal ", "United States ", "US"], ["Albany ", "NAB", "Albany NAS ", "United States ", "US"], ["Alma ", "AMN", "Gratiot Community ", "United States ", "US"], ["Alice ", "ALI", "International ", "United States ", "US"], ["Alamosa ", "ALS", "Municipal ", "United States ", "US"], ["Aspen ", "ASE", "Aspen ", "United States ", "US"], ["Akutan ", "KQA", "Akutan ", "United States ", "US"], ["Anita Bay ", "AIB", "Anita Bay ", "United States ", "US"], ["Apple Valley ", "APV", "Apple Valley ", "United States ", "US"], ["Alpine ", "ALE", "Alpine ", "United States ", "US"], ["Athens ", "ATO", "Ohio University ", "United States ", "US"], ["Arctic Village ", "ARC", "Arctic Village ", "United States ", "US"], ["Akiachak ", "KKI", "Spb ", "United States ", "US"], ["Akiak ", "AKI", "Akiak ", "United States ", "US"], ["Astoria ", "AST", "Astoria ", "United States ", "US"], ["Albany ", "CVO", "Albany ", "United States ", "US"], ["Anaktuvuk ", "AKP", "Anaktuvuk ", "United States ", "US"], ["Ardmore ", "ADM", "Ardmore Municipal Arpt ", "United States ", "US"], ["Arcata ", "ACV", "Arcata ", "United States ", "US"], ["Aniak ", "ANI", "Aniak ", "United States ", "US"], ["Ashley ", "ASY", "Ashley ", "United States ", "US"], ["Algona ", "AXG", "Algona ", "United States ", "US"], ["Angel Fire ", "AXX", "Angel Fire ", "United States ", "US"], ["Antlers ", "ATE", "Antlers ", "United States ", "US"], ["Anacortes ", "OTS", "Anacortes ", "United States ", "US"], ["Anderson ", "AND", "Anderson ", "United States ", "US"], ["Akhiok ", "AKK", "Akhiok SPB ", "United States ", "US"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Which city and country is the Alton airport at?	[]	2,048	Answer:
Table AIRPORTS: [["City", "AirportCode", "AirportName", "Country", "CountryAbbrev"], ["Alton ", "ALN", "Alton ", "United States ", "US"], ["Abingdon ", "VJI", "Virginia Highlands ", "United States ", "US"], ["Alexandria ", "ESF", "Esler Field ", "United States ", "US"], ["Akron/Canton ", "AKC", "Fulton International ", "United States ", "US"], [" Alexander City AL ", "ALX", "Thomas C Russell Fld ", "United States ", "US"], ["Akron CO ", "AKO", "Colorado Plains Regional Airport ", "United States ", "US"], ["Arapahoe ", "AHF", "Municipal ", "United States ", "US"], ["Anchorage ", "MRI", "Merrill Field ", "United States ", "US"], ["Aberdeen ", "ABR", "Municipal ", "United States ", "US"], ["Aberdeen ", "APG", "Phillips AAF ", "United States ", "US "], [" Alexandria LA ", "AEX", "Alexandria International ", "United States ", "US"], ["Abilene ", "DYS", "Dyess AFB ", "United States ", "US"], ["Anchorage ", "EDF", "Elmendorf Afb ", "United States ", "US"], ["Anchorage ", "ANC", "Ted Stevens Anchorage International Airport ", "United States ", "US"], ["Alamogordo ", "HMN", "Holloman AFB ", "United States ", "US"], ["Anniston ", "RLI", "Reilly AHP ", "United States ", "US"], ["Angola ", "ANQ", "Tri-State Steuben Cty ", "United States ", "US"], ["Abilene ", "ABI", "Municipal ", "United States ", "US"], ["Amchitka ", "AHT", "Amchitka ", "United States ", "US"], ["Ardmore ", "AHD", "Downtown ", "United States ", "US"], ["Athens ", "MMI", "McMinn County ", "United States ", "US"], [" Anniston AL ", "ANB", "Anniston Metropolitan ", "United States ", "US"], ["Anniston ", "QAW", "Ft Mcclellan Bus Trml ", "United States ", "US"], ["Alexandria Bay ", "AXB", "Alexandria Bay ", "United States ", "US"], ["Adrian ", "ADG", "Lenawee County ", "United States ", "US"], ["Apalachicola ", "AAF", "Municipal ", "United States ", "US"], ["Alpena ", "APN", "Alpena County Regional ", "United States ", "US"], ["Anaheim ", "ANA", "Orange County Steel Salvage Heliport ", "United States ", "US"], [" Aliceville AL ", "AIV", "George Downer ", "United States ", "US"], ["Afton ", "AFO", "Municipal ", "United States ", "US"], ["Alyeska ", "AQY", "Alyeska ", "United States ", "US"], [" Amarillo ", "AMA", "Rick Husband Amarillo International ", "United States ", "US"], ["Albany ", "ABY", "Dougherty County ", "United States ", "US"], ["Alamogordo ", "ALM", "Municipal ", "United States ", "US"], ["Amery ", "AHH", "Municipal ", "United States ", "US"], ["Ashland ", "ASX", "Ashland ", "United States ", "US"], ["Arlington Heights ", "JLH", "US Army Heliport ", "United States ", "US"], ["Akron/Canton OH ", "CAK", "Akron/canton Regional ", "United States ", "US"], ["Amityville ", "AYZ", "Zahns ", "United States ", "US"], ["Albuquerque ", "ABQ", "Albuquerque International ", "United States ", "US"], ["Anguilla ", "RFK", "Rollang Field ", "United States ", "US"], ["Aleneva ", "AED", "Aleneva ", "United States ", "US"], [" Neptune ", "ARX", "Asbury Park ", "United States ", "US"], ["Albany ", "ALB", "Albany International ", "United States ", "US"], ["Anderson ", "AID", "Municipal ", "United States ", "US"], ["Amarillo ", "TDW", "Tradewind ", "United States ", "US"], ["Altus ", "AXS", "Municipal ", "United States ", "US"], ["Alakanuk ", "AUK", "Alakanuk ", "United States ", "US"], ["Allakaket ", "AET", "Allakaket ", "United States ", "US"], ["Aleknagik ", "WKK", "Aleknagik ", "United States ", "US"], ["Artesia ", "ATS", "Artesia ", "United States ", "US"], ["Alitak ", "ALZ", "Alitak SPB ", "United States ", "US"], ["Aiken ", "AIK", "Municipal ", "United States ", "US"], ["Ames ", "AMW", "Ames ", "United States ", "US"], ["Adak Island ", "ADK", "Adak Island Ns ", "United States ", "US"], ["Annapolis ", "ANP", "Lee ", "United States ", "US"], ["Alameda ", "NGZ", "NAS ", "United States ", "US"], ["Albert Lea ", "AEL", "Albert Lea ", "United States ", "US"], ["Alexandria ", "AXN", "Alexandria ", "United States ", "US"], ["Anacostia ", "NDV", "USN Heliport ", "United States ", "US"], ["Alamosa ", "ALS", "Municipal ", "United States ", "US"], ["Altus ", "LTS", "Altus AFB ", "United States ", "US"], ["Ambler ", "ABL", "Ambler ", "United States ", "US"], ["Angoon ", "AGN", "Angoon ", "United States ", "US"], ["Ardmore ", "ADM", "Ardmore Municipal Arpt ", "United States ", "US"], ["Athens ", "AHN", "Athens ", "United States ", "US"], ["Anvik ", "ANV", "Anvik ", "United States ", "US"], [" Ann Arbor MI ", "ARB", "Municipal ", "United States ", "US"], ["Anita Bay ", "AIB", "Anita Bay ", "United States ", "US"], ["Amook ", "AOS", "Amook ", "United States ", "US"], ["Apple Valley ", "APV", "Apple Valley ", "United States ", "US"], ["Ainsworth ", "ANW", "Ainsworth ", "United States ", "US"], ["Alma ", "AMN", "Gratiot Community ", "United States ", "US"], ["Alice ", "ALI", "International ", "United States ", "US"], ["Appleton ", "ATW", "Outagamie County ", "United States ", "US"], ["Akutan ", "KQA", "Akutan ", "United States ", "US"], ["Akiachak ", "KKI", "Spb ", "United States ", "US"], ["Athens ", "ATO", "Ohio University ", "United States ", "US"], ["Andrews ", "ADR", "Andrews ", "United States ", "US"], ["Akiak ", "AKI", "Akiak ", "United States ", "US"], ["Annette Island ", "ANN", "Annette Island ", "United States ", "US"], ["Anaktuvuk ", "AKP", "Anaktuvuk ", "United States ", "US"], ["Albany ", "NAB", "Albany NAS ", "United States ", "US"], ["Astoria ", "AST", "Astoria ", "United States ", "US"], ["Aspen ", "ASE", "Aspen ", "United States ", "US"], ["Arctic Village ", "ARC", "Arctic Village ", "United States ", "US"], ["Albany ", "CVO", "Albany ", "United States ", "US"], ["Arcata ", "ACV", "Arcata ", "United States ", "US"], ["Alpine ", "ALE", "Alpine ", "United States ", "US"], ["Aniak ", "ANI", "Aniak ", "United States ", "US"], ["Algona ", "AXG", "Algona ", "United States ", "US"], ["Anacortes ", "OTS", "Anacortes ", "United States ", "US"], ["Akhiok ", "AKK", "Akhiok SPB ", "United States ", "US"], ["Angel Fire ", "AXX", "Angel Fire ", "United States ", "US"], ["Ashley ", "ASY", "Ashley ", "United States ", "US"], ["Antlers ", "ATE", "Antlers ", "United States ", "US"], ["Ada ", "ADT", "Ada ", "United States ", "US"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:Give the city and country for the Alton airport.	[]	2,048	Answer:
Table AIRPORTS: [["City", "AirportCode", "AirportName", "Country", "CountryAbbrev"], ["Akron CO ", "AKO", "Colorado Plains Regional Airport ", "United States ", "US"], ["Anchorage ", "ANC", "Ted Stevens Anchorage International Airport ", "United States ", "US"], ["Akhiok ", "AKK", "Akhiok SPB ", "United States ", "US"], ["Akiak ", "AKI", "Akiak ", "United States ", "US"], ["Akutan ", "KQA", "Akutan ", "United States ", "US"], ["Anchorage ", "EDF", "Elmendorf Afb ", "United States ", "US"], ["Akiachak ", "KKI", "Spb ", "United States ", "US"], ["Anaktuvuk ", "AKP", "Anaktuvuk ", "United States ", "US"], ["Akron/Canton ", "AKC", "Fulton International ", "United States ", "US"], ["Alakanuk ", "AUK", "Alakanuk ", "United States ", "US"], ["Alamogordo ", "HMN", "Holloman AFB ", "United States ", "US"], ["Aberdeen ", "APG", "Phillips AAF ", "United States ", "US "], ["Afton ", "AFO", "Municipal ", "United States ", "US"], ["Adak Island ", "ADK", "Adak Island Ns ", "United States ", "US"], ["Anchorage ", "MRI", "Merrill Field ", "United States ", "US"], ["Altus ", "LTS", "Altus AFB ", "United States ", "US"], ["Abilene ", "DYS", "Dyess AFB ", "United States ", "US"], ["Amchitka ", "AHT", "Amchitka ", "United States ", "US"], ["Arapahoe ", "AHF", "Municipal ", "United States ", "US"], ["Alameda ", "NGZ", "NAS ", "United States ", "US"], ["Ardmore ", "AHD", "Downtown ", "United States ", "US"], ["Allakaket ", "AET", "Allakaket ", "United States ", "US"], ["Aleknagik ", "WKK", "Aleknagik ", "United States ", "US"], [" Alexandria LA ", "AEX", "Alexandria International ", "United States ", "US"], ["Alyeska ", "AQY", "Alyeska ", "United States ", "US"], ["Anniston ", "RLI", "Reilly AHP ", "United States ", "US"], ["Anaheim ", "ANA", "Orange County Steel Salvage Heliport ", "United States ", "US"], ["Anniston ", "QAW", "Ft Mcclellan Bus Trml ", "United States ", "US"], ["Aberdeen ", "ABR", "Municipal ", "United States ", "US"], ["Alitak ", "ALZ", "Alitak SPB ", "United States ", "US"], [" Neptune ", "ARX", "Asbury Park ", "United States ", "US"], ["Artesia ", "ATS", "Artesia ", "United States ", "US"], ["Angoon ", "AGN", "Angoon ", "United States ", "US"], ["Athens ", "AHN", "Athens ", "United States ", "US"], ["Anvik ", "ANV", "Anvik ", "United States ", "US"], ["Ashland ", "ASX", "Ashland ", "United States ", "US"], ["Arlington Heights ", "JLH", "US Army Heliport ", "United States ", "US"], ["Ardmore ", "ADM", "Ardmore Municipal Arpt ", "United States ", "US"], ["Amook ", "AOS", "Amook ", "United States ", "US"], ["Albuquerque ", "ABQ", "Albuquerque International ", "United States ", "US"], ["Aleneva ", "AED", "Aleneva ", "United States ", "US"], ["Aiken ", "AIK", "Municipal ", "United States ", "US"], ["Ambler ", "ABL", "Ambler ", "United States ", "US"], ["Albany ", "ALB", "Albany International ", "United States ", "US"], ["Athens ", "ATO", "Ohio University ", "United States ", "US"], ["Anguilla ", "RFK", "Rollang Field ", "United States ", "US"], ["Akron/Canton OH ", "CAK", "Akron/canton Regional ", "United States ", "US"], ["Arctic Village ", "ARC", "Arctic Village ", "United States ", "US"], ["Alexandria ", "ESF", "Esler Field ", "United States ", "US"], ["Anita Bay ", "AIB", "Anita Bay ", "United States ", "US"], ["Anacostia ", "NDV", "USN Heliport ", "United States ", "US"], [" Alexander City AL ", "ALX", "Thomas C Russell Fld ", "United States ", "US"], ["Alexandria Bay ", "AXB", "Alexandria Bay ", "United States ", "US"], ["Albany ", "CVO", "Albany ", "United States ", "US"], [" Anniston AL ", "ANB", "Anniston Metropolitan ", "United States ", "US"], ["Alamogordo ", "ALM", "Municipal ", "United States ", "US"], ["Angola ", "ANQ", "Tri-State Steuben Cty ", "United States ", "US"], ["Altus ", "AXS", "Municipal ", "United States ", "US"], ["Abingdon ", "VJI", "Virginia Highlands ", "United States ", "US"], ["Apalachicola ", "AAF", "Municipal ", "United States ", "US"], ["Abilene ", "ABI", "Municipal ", "United States ", "US"], ["Alamosa ", "ALS", "Municipal ", "United States ", "US"], ["Aniak ", "ANI", "Aniak ", "United States ", "US"], ["Algona ", "AXG", "Algona ", "United States ", "US"], ["Albany ", "NAB", "Albany NAS ", "United States ", "US"], ["Ada ", "ADT", "Ada ", "United States ", "US"], ["Arcata ", "ACV", "Arcata ", "United States ", "US"], ["Ames ", "AMW", "Ames ", "United States ", "US"], ["Alice ", "ALI", "International ", "United States ", "US"], ["Ainsworth ", "ANW", "Ainsworth ", "United States ", "US"], ["Annapolis ", "ANP", "Lee ", "United States ", "US"], ["Amery ", "AHH", "Municipal ", "United States ", "US"], ["Amarillo ", "TDW", "Tradewind ", "United States ", "US"], ["Astoria ", "AST", "Astoria ", "United States ", "US"], ["Alexandria ", "AXN", "Alexandria ", "United States ", "US"], ["Amityville ", "AYZ", "Zahns ", "United States ", "US"], ["Alliance ", "AIA", "Alliance ", "United States ", "US"], ["Alpena ", "APN", "Alpena County Regional ", "United States ", "US"], ["Angel Fire ", "AXX", "Angel Fire ", "United States ", "US"], ["Adrian ", "ADG", "Lenawee County ", "United States ", "US"], ["Albert Lea ", "AEL", "Albert Lea ", "United States ", "US"], ["Athens ", "MMI", "McMinn County ", "United States ", "US"], [" Aliceville AL ", "AIV", "George Downer ", "United States ", "US"], ["Alpine ", "ALE", "Alpine ", "United States ", "US"], ["Alton ", "ALN", "Alton ", "United States ", "US"], ["Aspen ", "ASE", "Aspen ", "United States ", "US"], ["Alma ", "AMN", "Gratiot Community ", "United States ", "US"], ["Annette Island ", "ANN", "Annette Island ", "United States ", "US"], ["Andrews ", "ADR", "Andrews ", "United States ", "US"], ["Anderson ", "AID", "Municipal ", "United States ", "US"], [" Amarillo ", "AMA", "Rick Husband Amarillo International ", "United States ", "US"], ["Anthony ", "ANY", "Anthony ", "United States ", "US"], ["Apple Valley ", "APV", "Apple Valley ", "United States ", "US"], [" Ann Arbor MI ", "ARB", "Municipal ", "United States ", "US"], ["Albany ", "ABY", "Dougherty County ", "United States ", "US"], ["Appleton ", "ATW", "Outagamie County ", "United States ", "US"], ["Anacortes ", "OTS", "Anacortes ", "United States ", "US"]]	You are a question-answering model specialized in tabular data. I will provide a table in a list-of-lists format (where the first row is the header) and a single natural language question.You must return the exact table cells that directly answer the provided question. Important guidelines: - Your response must be an array of arrays of strings. - If the question requests an aggregation (average, sum, count, etc.), return ONLY the aggregated value(s), formatted as a list containing one tuple. - The answer must contain ONLY values from the provided table or the aggregated value explicitly requested. - Do NOT include headers or column names in your answer. - Do NOT include explanations or reasoning in your answer. - Do NOT repeat these instructions in your answer. - Examples Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "Show all information about each employee" Answer: [ ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"] ] Table: [["Employee_ID", "Name", "Department", "Salary"], ["101", "Alice", "HR", "50000"], ["102", "Bob", "Engineering", "75000"], ["103", "Charlie", "Marketing", "60000"]] Question: "What is the average salary?" Answer: [["61666.67"]] Question:What is the airport name for airport 'AKO'?	[["Colorado Plains Regional Airport "]]	2,048	Answer:

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