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stringlengths 130
6.32k
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stringlengths 1.42k
1.58k
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Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"]] | 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 singers do we have? | [["6"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"]] | 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 singers? | [["6"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"]] | 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:Show name, country, age for all singers ordered by age from the oldest to the youngest. | [["Joe Sharp", "Netherlands", "52"], ["John Nizinik", "France", "43"], ["Rose White", "France", "41"], ["Timbaland", "United States", "32"], ["Justin Brown", "France", "29"], ["Tribal King", "France", "25"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"]] | 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, countries, and ages for every singer in descending order of age? | [["Joe Sharp", "Netherlands", "52"], ["John Nizinik", "France", "43"], ["Rose White", "France", "41"], ["Timbaland", "United States", "32"], ["Justin Brown", "France", "29"], ["Tribal King", "France", "25"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"]] | 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, minimum, and maximum age of all singers from France? | [["34.5", "25", "43"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"]] | 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, minimum, and maximum age for all French singers? | [["34.5", "25", "43"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"]] | 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:Show the name and the release year of the song by the youngest singer. | [["Love", "2016"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"]] | 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 release years for all the songs of the youngest singer? | [["Love", "2016"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"]] | 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 distinct countries where singers above age 20 are from? | [["Netherlands"], ["United States"], ["France"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"]] | 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 countries with singers above age 20? | [["Netherlands"], ["United States"], ["France"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"]] | 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:Show all countries and the number of singers in each country. | [["France", "4"], ["Netherlands", "1"], ["United States", "1"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"]] | 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 singers are from each country? | [["France", "4"], ["Netherlands", "1"], ["United States", "1"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"]] | 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 song names by singers above the average age. | [["You"], ["Sun"], ["Gentleman"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"]] | 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 song names by singers who are older than average? | [["You"], ["Sun"], ["Gentleman"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"]] | 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:Show location and name for all stadiums with a capacity between 5000 and 10000. | [] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"]] | 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 locations and names of all stations with capacity between 5000 and 10000? | [] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"]] | 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 capacity and the average of all stadiums ? | [["52500", "730"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"]] | 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 and maximum capacities for all stadiums ? | [["10621.666666666666", "52500"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"]] | 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 and capacity for the stadium with highest average attendance? | [["Stark's Park", "10104"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"]] | 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 and capacity for the stadium with the highest average attendance? | [["Stark's Park", "10104"]] | 2,048 | Answer: |
Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"], ["3", "Home Visits", "Bleeding Love", "2", "2015"], ["4", "Week 1", "Wide Awake", "10", "2014"], ["1", "Auditions", "Free choice", "1", "2014"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"]] | 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 concerts are there in year 2014 or 2015? | [["6"]] | 2,048 | Answer: |
Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"], ["3", "Home Visits", "Bleeding Love", "2", "2015"], ["4", "Week 1", "Wide Awake", "10", "2014"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["1", "Auditions", "Free choice", "1", "2014"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"]] | 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 concerts occurred in 2014 or 2015? | [["6"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["4", "Week 1", "Wide Awake", "10", "2014"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["1", "Auditions", "Free choice", "1", "2014"], ["3", "Home Visits", "Bleeding Love", "2", "2015"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"]] | 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:Show the stadium name and the number of concerts in each stadium. | [["Stark's Park", "1"], ["Glebe Park", "1"], ["Somerset Park", "2"], ["Recreation Park", "1"], ["Balmoor", "1"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["4", "Week 1", "Wide Awake", "10", "2014"], ["6", "Week 2", "Party All Night", "7", "2015"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["3", "Home Visits", "Bleeding Love", "2", "2015"], ["1", "Auditions", "Free choice", "1", "2014"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"]] | 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 each stadium, how many concerts play there? | [["Stark's Park", "1"], ["Glebe Park", "1"], ["Somerset Park", "2"], ["Recreation Park", "1"], ["Balmoor", "1"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["4", "Week 1", "Wide Awake", "10", "2014"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["1", "Auditions", "Free choice", "1", "2014"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"], ["3", "Home Visits", "Bleeding Love", "2", "2015"]] | 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:Show the stadium name and capacity with most number of concerts in year 2014 or after. | [["Somerset Park", "11998"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["4", "Week 1", "Wide Awake", "10", "2014"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["1", "Auditions", "Free choice", "1", "2014"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"], ["3", "Home Visits", "Bleeding Love", "2", "2015"]] | 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 and capacity of the stadium with the most concerts after 2013 ? | [["Somerset Park", "11998"]] | 2,048 | Answer: |
Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"], ["4", "Week 1", "Wide Awake", "10", "2014"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["3", "Home Visits", "Bleeding Love", "2", "2015"], ["1", "Auditions", "Free choice", "1", "2014"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"]] | 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 year has most number of concerts? | [["2015"]] | 2,048 | Answer: |
Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"], ["4", "Week 1", "Wide Awake", "10", "2014"], ["3", "Home Visits", "Bleeding Love", "2", "2015"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["1", "Auditions", "Free choice", "1", "2014"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"]] | 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 year that had the most concerts? | [["2015"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"], ["1", "Auditions", "Free choice", "1", "2014"], ["4", "Week 1", "Wide Awake", "10", "2014"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"], ["3", "Home Visits", "Bleeding Love", "2", "2015"]] | 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:Show the stadium names without any concert. | [["Bayview Stadium"], ["Hampden Park"], ["Forthbank Stadium"], ["Gayfield Park"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["4", "Week 1", "Wide Awake", "10", "2014"], ["1", "Auditions", "Free choice", "1", "2014"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"], ["3", "Home Visits", "Bleeding Love", "2", "2015"]] | 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 stadiums without any concerts? | [["Bayview Stadium"], ["Hampden Park"], ["Forthbank Stadium"], ["Gayfield Park"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"]] | 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:Show countries where a singer above age 40 and a singer below 30 are from. | [["France"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["4", "Week 1", "Wide Awake", "10", "2014"], ["1", "Auditions", "Free choice", "1", "2014"], ["6", "Week 2", "Party All Night", "7", "2015"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"], ["3", "Home Visits", "Bleeding Love", "2", "2015"]] | 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:Show names for all stadiums except for stadiums having a concert in year 2014. | [["Balmoor"], ["Bayview Stadium"], ["Forthbank Stadium"], ["Gayfield Park"], ["Hampden Park"], ["Recreation Park"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"], ["4", "Week 1", "Wide Awake", "10", "2014"], ["1", "Auditions", "Free choice", "1", "2014"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"], ["5", "Week 1", "Happy Tonight", "9", "2015"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["3", "Home Visits", "Bleeding Love", "2", "2015"]] | 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 stadiums that did not have a concert in 2014? | [["Balmoor"], ["Bayview Stadium"], ["Forthbank Stadium"], ["Gayfield Park"], ["Hampden Park"], ["Recreation Park"]] | 2,048 | Answer: |
Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"], ["1", "Auditions", "Free choice", "1", "2014"], ["4", "Week 1", "Wide Awake", "10", "2014"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["3", "Home Visits", "Bleeding Love", "2", "2015"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"]]Table singer_in_concert: [["concert_ID", "Singer_ID"], ["3", "5"], ["5", "3"], ["2", "3"], ["1", "3"], ["1", "2"], ["4", "4"], ["2", "6"], ["6", "2"], ["5", "6"], ["1", "5"]] | 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:Show the name and theme for all concerts and the number of singers in each concert. | [["Auditions", "Free choice", "3"], ["Super bootcamp", "Free choice 2", "2"], ["Home Visits", "Bleeding Love", "1"], ["Week 1", "Wide Awake", "1"], ["Week 1", "Happy Tonight", "2"], ["Week 2", "Party All Night", "1"]] | 2,048 | Answer: |
Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"], ["1", "Auditions", "Free choice", "1", "2014"], ["4", "Week 1", "Wide Awake", "10", "2014"], ["3", "Home Visits", "Bleeding Love", "2", "2015"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"]]Table singer_in_concert: [["concert_ID", "Singer_ID"], ["6", "2"], ["3", "5"], ["5", "3"], ["2", "6"], ["2", "3"], ["5", "6"], ["1", "3"], ["4", "4"], ["1", "2"], ["1", "5"]] | 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 , themes , and number of singers for every concert ? | [["Auditions", "Free choice", "3"], ["Super bootcamp", "Free choice 2", "2"], ["Home Visits", "Bleeding Love", "1"], ["Week 1", "Wide Awake", "1"], ["Week 1", "Happy Tonight", "2"], ["Week 2", "Party All Night", "1"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"]]Table singer_in_concert: [["concert_ID", "Singer_ID"], ["1", "3"], ["5", "3"], ["3", "5"], ["1", "2"], ["2", "3"], ["6", "2"], ["2", "6"], ["1", "5"], ["5", "6"], ["4", "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:List singer names and number of concerts for each singer. | [["Timbaland", "2"], ["Justin Brown", "3"], ["Rose White", "1"], ["John Nizinik", "2"], ["Tribal King", "2"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"]]Table singer_in_concert: [["concert_ID", "Singer_ID"], ["2", "3"], ["1", "3"], ["2", "6"], ["1", "2"], ["3", "5"], ["6", "2"], ["5", "3"], ["4", "4"], ["5", "6"], ["1", "5"]] | 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 singers and number of concerts for each person? | [["Timbaland", "2"], ["Justin Brown", "3"], ["Rose White", "1"], ["John Nizinik", "2"], ["Tribal King", "2"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["4", "Week 1", "Wide Awake", "10", "2014"], ["1", "Auditions", "Free choice", "1", "2014"]]Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"], ["3", "Home Visits", "Bleeding Love", "2", "2015"]]Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"]]Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"]]Table singer_in_concert: [["concert_ID", "Singer_ID"], ["2", "3"], ["1", "2"], ["4", "4"], ["1", "3"], ["3", "5"], ["5", "3"], ["2", "6"], ["6", "2"], ["1", "5"], ["5", "6"]] | 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 singer names in concerts in year 2014. | [["Timbaland"], ["Justin Brown"], ["John Nizinik"], ["Justin Brown"], ["Tribal King"], ["Rose White"]] | 2,048 | Answer: |
Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["1", "Auditions", "Free choice", "1", "2014"]]Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["4", "Week 1", "Wide Awake", "10", "2014"], ["3", "Home Visits", "Bleeding Love", "2", "2015"]]Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"]]Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["5", "Week 1", "Happy Tonight", "9", "2015"]]Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"]]Table singer_in_concert: [["concert_ID", "Singer_ID"], ["2", "3"], ["1", "2"], ["1", "3"], ["4", "4"], ["3", "5"], ["2", "6"], ["5", "3"], ["6", "2"], ["1", "5"], ["5", "6"]] | 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 singers who performed in a concert in 2014? | [["Timbaland"], ["Justin Brown"], ["John Nizinik"], ["Justin Brown"], ["Tribal King"], ["Rose White"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"]] | 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 and nation of the singer who have a song having 'Hey' in its name? | [["Justin Brown", "France"]] | 2,048 | Answer: |
Table singer: [["Singer_ID", "Name", "Country", "Song_Name", "Song_release_year", "Age", "Is_male"], ["3", "Justin Brown", "France", "Hey Oh", "2013", "29", "T"], ["2", "Timbaland", "United States", "Dangerous", "2008", "32", "T"], ["1", "Joe Sharp", "Netherlands", "You", "1992", "52", "F"], ["6", "Tribal King", "France", "Love", "2016", "25", "T"], ["5", "John Nizinik", "France", "Gentleman", "2014", "43", "T"], ["4", "Rose White", "France", "Sun", "2003", "41", "F"]] | 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 and country of origin of every singer who has a song with the word 'Hey' in its title? | [["Justin Brown", "France"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["4", "Week 1", "Wide Awake", "10", "2014"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["5", "Week 1", "Happy Tonight", "9", "2015"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["1", "Auditions", "Free choice", "1", "2014"], ["3", "Home Visits", "Bleeding Love", "2", "2015"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"]] | 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 name and location of the stadiums which some concerts happened in the years of both 2014 and 2015. | [["Somerset Park", "Ayr United"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["4", "Week 1", "Wide Awake", "10", "2014"], ["3", "Home Visits", "Bleeding Love", "2", "2015"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["5", "Week 1", "Happy Tonight", "9", "2015"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["1", "Auditions", "Free choice", "1", "2014"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"]] | 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 locations of the stadiums that had concerts that occurred in both 2014 and 2015? | [["Somerset Park", "Ayr United"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["4", "Week 1", "Wide Awake", "10", "2014"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["1", "Auditions", "Free choice", "1", "2014"], ["3", "Home Visits", "Bleeding Love", "2", "2015"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"]] | 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 number of concerts happened in the stadium with the highest capacity . | [["0"]] | 2,048 | Answer: |
Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["3", "East Fife", "Bayview Stadium", "2000", "1980", "533", "864"], ["5", "Stirling Albion", "Forthbank Stadium", "3808", "1125", "404", "642"], ["4", "Queen's Park", "Hampden Park", "52500", "1763", "466", "730"], ["7", "Alloa Athletic", "Recreation Park", "3100", "1057", "331", "637"], ["10", "Brechin City", "Glebe Park", "3960", "780", "315", "552"], ["2", "Ayr United", "Somerset Park", "11998", "2363", "1057", "1477"], ["6", "Arbroath", "Gayfield Park", "4125", "921", "411", "638"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["4", "Week 1", "Wide Awake", "10", "2014"]]Table stadium: [["Stadium_ID", "Location", "Name", "Capacity", "Highest", "Lowest", "Average"], ["9", "Peterhead", "Balmoor", "4000", "837", "400", "615"], ["1", "Raith Rovers", "Stark's Park", "10104", "4812", "1294", "2106"]]Table concert: [["concert_ID", "concert_Name", "Theme", "Stadium_ID", "Year"], ["6", "Week 2", "Party All Night", "7", "2015"], ["5", "Week 1", "Happy Tonight", "9", "2015"], ["3", "Home Visits", "Bleeding Love", "2", "2015"], ["1", "Auditions", "Free choice", "1", "2014"], ["2", "Super bootcamp", "Free choice 2", "2", "2014"]] | 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 number of concerts that occurred in the stadium with the largest capacity ? | [["0"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"], ["2002", "dog", "2", "13.4"], ["2001", "cat", "3", "12.0"]] | 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 number of pets whose weight is heavier than 10. | [["2"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"], ["2002", "dog", "2", "13.4"], ["2001", "cat", "3", "12.0"]] | 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 pets have a greater weight than 10? | [["2"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"], ["2003", "dog", "1", "9.3"], ["2001", "cat", "3", "12.0"]] | 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 weight of the youngest dog. | [["9.3"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"], ["2003", "dog", "1", "9.3"], ["2001", "cat", "3", "12.0"]] | 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 youngest dog weigh? | [["9.3"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"], ["2002", "dog", "2", "13.4"], ["2001", "cat", "3", "12.0"]] | 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 maximum weight for each type of pet. List the maximum weight and pet type. | [["12.0", "cat"], ["13.4", "dog"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"], ["2002", "dog", "2", "13.4"], ["2001", "cat", "3", "12.0"]] | 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 maximum weight and type for each type of pet. | [["12.0", "cat"], ["13.4", "dog"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"], ["1002", "2002"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"]] | 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 number of pets owned by students who are older than 20. | [["0"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"], ["1001", "2001"]] | 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 pets are owned by students that have an age greater than 20? | [["0"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2001", "cat", "3", "12.0"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"], ["1001", "2001"]] | 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 number of dog pets that are raised by female students (with sex F). | [["2"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"], ["2002", "dog", "2", "13.4"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2001", "cat", "3", "12.0"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"], ["1001", "2001"]] | 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 dog pets are raised by female students? | [["2"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"], ["2002", "dog", "2", "13.4"], ["2001", "cat", "3", "12.0"]] | 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 number of distinct type of pets. | [["2"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"], ["2002", "dog", "2", "13.4"], ["2001", "cat", "3", "12.0"]] | 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 different types of pet are there? | [["2"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2001", "cat", "3", "12.0"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"], ["1002", "2003"], ["1002", "2002"]] | 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 first name of students who have cat or dog pet. | [["Linda"], ["Tracy"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"], ["2003", "dog", "1", "9.3"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2001", "cat", "3", "12.0"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"]] | 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 first names of every student who has a cat or dog as a pet? | [["Linda"], ["Tracy"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2001", "cat", "3", "12.0"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"]] | 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 first name of students who have both cat and dog pets . | [] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"], ["2003", "dog", "1", "9.3"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2001", "cat", "3", "12.0"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]] | 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 students' first names who have both cats and dogs as pets? | [] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"], ["2003", "dog", "1", "9.3"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2001", "cat", "3", "12.0"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"], ["1001", "2001"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]] | 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 major and age of students who do not have a cat pet. | [["600", "19"], ["600", "21"], ["600", "20"], ["600", "26"], ["600", "18"], ["600", "18"], ["600", "20"], ["600", "19"], ["600", "17"], ["600", "22"], ["600", "20"], ["600", "18"], ["600", "16"], ["600", "17"], ["600", "27"], ["600", "20"], ["600", "18"], ["520", "22"], ["520", "19"], ["540", "17"], ["520", "20"], ["540", "18"], ["520", "18"], ["520", "19"], ["520", "18"], ["550", "20"], ["100", "17"], ["550", "21"], ["550", "20"], ["550", "20"], ["550", "18"], ["50", "18"], ["50", "26"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2001", "cat", "3", "12.0"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]] | 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 major is every student who does not own a cat as a pet, and also how old are they? | [["600", "19"], ["600", "21"], ["600", "20"], ["600", "26"], ["600", "18"], ["600", "18"], ["600", "20"], ["600", "19"], ["600", "17"], ["600", "22"], ["600", "20"], ["600", "18"], ["600", "16"], ["600", "17"], ["600", "27"], ["600", "20"], ["600", "18"], ["520", "22"], ["520", "19"], ["540", "17"], ["520", "20"], ["540", "18"], ["520", "18"], ["520", "19"], ["520", "18"], ["550", "20"], ["100", "17"], ["550", "21"], ["550", "20"], ["550", "20"], ["550", "18"], ["50", "18"], ["50", "26"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2001", "cat", "3", "12.0"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"], ["2003", "dog", "1", "9.3"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"], ["1002", "2003"], ["1002", "2002"]] | 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 id of students who do not have a cat pet. | [["1002"], ["1003"], ["1004"], ["1005"], ["1006"], ["1007"], ["1008"], ["1009"], ["1010"], ["1011"], ["1012"], ["1014"], ["1015"], ["1016"], ["1017"], ["1018"], ["1019"], ["1020"], ["1021"], ["1022"], ["1023"], ["1024"], ["1025"], ["1026"], ["1027"], ["1028"], ["1029"], ["1030"], ["1031"], ["1032"], ["1033"], ["1034"], ["1035"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2001", "cat", "3", "12.0"], ["2002", "dog", "2", "13.4"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"], ["1002", "2003"], ["1002", "2002"]] | 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 of the students who do not own cats as pets? | [["1002"], ["1003"], ["1004"], ["1005"], ["1006"], ["1007"], ["1008"], ["1009"], ["1010"], ["1011"], ["1012"], ["1014"], ["1015"], ["1016"], ["1017"], ["1018"], ["1019"], ["1020"], ["1021"], ["1022"], ["1023"], ["1024"], ["1025"], ["1026"], ["1027"], ["1028"], ["1029"], ["1030"], ["1031"], ["1032"], ["1033"], ["1034"], ["1035"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2001", "cat", "3", "12.0"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"], ["1002", "2002"]] | 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 first name and age of students who have a dog but do not have a cat as a pet. | [["Tracy", "19"], ["Tracy", "19"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"], ["2003", "dog", "1", "9.3"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2001", "cat", "3", "12.0"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"]] | 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 first name of every student who has a dog but does not have a cat? | [["Tracy", "19"], ["Tracy", "19"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"], ["2003", "dog", "1", "9.3"], ["2001", "cat", "3", "12.0"]] | 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 type and weight of the youngest pet. | [["dog", "9.3"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"], ["2003", "dog", "1", "9.3"], ["2001", "cat", "3", "12.0"]] | 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 type of pet is the youngest animal, and how much does it weigh? | [["dog", "9.3"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"], ["2002", "dog", "2", "13.4"], ["2001", "cat", "3", "12.0"]] | 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 id and weight of all pets whose age is older than 1. | [["2001", "12.0"], ["2002", "13.4"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"], ["2003", "dog", "1", "9.3"], ["2001", "cat", "3", "12.0"]] | 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 id and weight of every pet who is older than 1? | [["2001", "12.0"], ["2002", "13.4"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"], ["2003", "dog", "1", "9.3"], ["2001", "cat", "3", "12.0"]] | 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 average and maximum age for each type of pet. | [["3.0", "3", "cat"], ["1.5", "2", "dog"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"], ["2003", "dog", "1", "9.3"], ["2001", "cat", "3", "12.0"]] | 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 and maximum age for each pet type? | [["3.0", "3", "cat"], ["1.5", "2", "dog"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"], ["2002", "dog", "2", "13.4"], ["2001", "cat", "3", "12.0"]] | 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 average weight for each pet type. | [["12.0", "cat"], ["11.350000000000001", "dog"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"], ["2003", "dog", "1", "9.3"], ["2001", "cat", "3", "12.0"]] | 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 for each type of pet? | [["12.0", "cat"], ["11.350000000000001", "dog"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"], ["1002", "2003"], ["1002", "2002"]] | 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 first name and age of students who have a pet. | [["Linda", "18"], ["Tracy", "19"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"], ["1002", "2002"]] | 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 first names and ages of the students who do have pets? | [["Linda", "18"], ["Tracy", "19"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"], ["1002", "2003"], ["1001", "2001"]] | 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 id of the pet owned by student whose last name is ‘Smith’. | [["2001"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"], ["1002", "2003"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]] | 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 id of the pet owned by the student whose last name is 'Smith'? | [["2001"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"], ["1001", "2001"], ["1002", "2002"]] | 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 number of pets for each student who has any pet and student id. | [["1", "1001"], ["2", "1002"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"], ["1002", "2003"], ["1002", "2002"]] | 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 students who have pets , how many pets does each student have ? list their ids instead of names . | [["1", "1001"], ["2", "1002"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"], ["1002", "2003"], ["1002", "2002"]] | 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 first name and gender of student who have more than one pet. | [["Tracy", "F"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"], ["1002", "2002"]] | 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 first name and gender of the all the students who have more than one pet? | [["Tracy", "F"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2001", "cat", "3", "12.0"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2002", "dog", "2", "13.4"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"], ["1001", "2001"]] | 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 last name of the student who has a cat that is age 3. | [["Smith"]] | 2,048 | Answer: |
Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2001", "cat", "3", "12.0"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"]]Table pets: [["PetID", "PetType", "pet_age", "weight"], ["2003", "dog", "1", "9.3"], ["2002", "dog", "2", "13.4"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"]] | 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 last name of the student who has a cat that is 3 years old? | [["Smith"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"], ["1001", "2001"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"]] | 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 average age of students who do not have any pet . | [["19.625"]] | 2,048 | Answer: |
Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1035", "Schmidt", "Sarah", "26", "F", "50", "5718", "WAS"], ["1001", "Smith", "Linda", "18", "F", "600", "1121", "BAL"], ["1031", "Smith", "Sarah", "20", "F", "550", "8772", "PHL"], ["1034", "Epp", "Eric", "18", "M", "50", "5718", "BOS"], ["1010", "Lee", "Derek", "17", "M", "600", "2192", "HOU"], ["1003", "Jones", "Shiela", "21", "F", "600", "7792", "WAS"], ["1024", "Prater", "Stacy", "18", "F", "540", "7271", "BAL"], ["1005", "Gompers", "Paul", "26", "M", "600", "1121", "YYZ"], ["1032", "Brown", "Eric", "20", "M", "550", "8772", "ATL"], ["1017", "Wilson", "Bruce", "27", "M", "600", "1148", "LON"], ["1007", "Apap", "Lisa", "18", "F", "600", "8918", "PIT"], ["1008", "Nelson", "Jandy", "20", "F", "600", "9172", "BAL"], ["1022", "Woods", "Michael", "17", "M", "540", "8722", "PHL"], ["1015", "Lee", "Susan", "16", "F", "600", "8721", "HKG"], ["1011", "Adams", "David", "22", "M", "600", "1148", "PHL"], ["1009", "Tai", "Eric", "19", "M", "600", "2192", "YYZ"], ["1012", "Davis", "Steven", "20", "M", "600", "7723", "PIT"], ["1014", "Norris", "Charles", "18", "M", "600", "8741", "DAL"], ["1033", "Simms", "William", "18", "M", "550", "8772", "NAR"], ["1018", "Leighton", "Michael", "20", "M", "600", "1121", "PIT"], ["1021", "Andreou", "George", "19", "M", "520", "8722", "NYC"], ["1025", "Goldman", "Mark", "18", "M", "520", "7134", "PIT"], ["1028", "Rugh", "Eric", "20", "M", "550", "2311", "ROC"], ["1006", "Schultz", "Andy", "18", "M", "600", "1148", "BAL"], ["1019", "Pang", "Arthur", "18", "M", "600", "2192", "WAS"], ["1023", "Shieber", "David", "20", "M", "520", "8722", "NYC"], ["1030", "Cheng", "Lisa", "21", "F", "550", "2311", "SFO"], ["1027", "Brody", "Paul", "18", "M", "520", "8723", "LOS"], ["1004", "Kumar", "Dinesh", "20", "M", "600", "8423", "CHI"], ["1029", "Han", "Jun", "17", "M", "100", "2311", "PEK"], ["1016", "Schwartz", "Mark", "17", "M", "600", "2192", "DET"]]Table has_pet: [["StuID", "PetID"], ["1001", "2001"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1002", "Kim", "Tracy", "19", "F", "600", "7712", "HKG"]]Table has_pet: [["StuID", "PetID"], ["1002", "2003"]]Table student: [["StuID", "LName", "Fname", "Age", "Sex", "Major", "Advisor", "city_code"], ["1026", "Pang", "Eric", "19", "M", "520", "7134", "HKG"], ["1020", "Thornton", "Ian", "22", "M", "520", "7271", "NYC"]]Table has_pet: [["StuID", "PetID"], ["1002", "2002"]] | 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 age for all students who do not own any pets ? | [["19.625"]] | 2,048 | Answer: |
Table CONTINENTS: [["ContId", "Continent"], ["3", "asia"], ["2", "europe"], ["4", "africa"], ["1", "america"], ["5", "australia"]] | 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 continents are there? | [["5"]] | 2,048 | Answer: |
Table CONTINENTS: [["ContId", "Continent"], ["3", "asia"], ["2", "europe"], ["4", "africa"], ["5", "australia"], ["1", "america"]] | 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 continents? | [["5"]] | 2,048 | Answer: |
Table CONTINENTS: [["ContId", "Continent"], ["2", "europe"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["11", "australia", "5"], ["6", "sweden", "2"], ["15", "brazil", "1"]]Table CONTINENTS: [["ContId", "Continent"], ["3", "asia"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"], ["2", "germany", "2"], ["12", "new zealand", "5"]]Table CONTINENTS: [["ContId", "Continent"], ["4", "africa"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["3", "france", "2"], ["5", "italy", "2"]]Table CONTINENTS: [["ContId", "Continent"], ["5", "australia"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["7", "uk", "2"], ["13", "egypt", "4"], ["8", "korea", "3"], ["14", "mexico", "1"], ["1", "usa", "1"]]Table CONTINENTS: [["ContId", "Continent"], ["1", "america"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["10", "nigeria", "4"], ["4", "japan", "3"]] | 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 does each continent have? List the continent id, continent name and the number of countries. | [["1", "america", "3"], ["2", "europe", "6"], ["3", "asia", "2"], ["4", "africa", "2"], ["5", "australia", "2"]] | 2,048 | Answer: |
Table CONTINENTS: [["ContId", "Continent"], ["2", "europe"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["15", "brazil", "1"]]Table CONTINENTS: [["ContId", "Continent"], ["4", "africa"], ["3", "asia"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["11", "australia", "5"], ["9", "russia", "2"], ["13", "egypt", "4"], ["6", "sweden", "2"], ["12", "new zealand", "5"], ["3", "france", "2"]]Table CONTINENTS: [["ContId", "Continent"], ["5", "australia"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["5", "italy", "2"], ["2", "germany", "2"], ["14", "mexico", "1"], ["8", "korea", "3"], ["4", "japan", "3"], ["7", "uk", "2"], ["10", "nigeria", "4"]]Table CONTINENTS: [["ContId", "Continent"], ["1", "america"]]Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["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:For each continent, list its id, name, and how many countries it has? | [["1", "america", "3"], ["2", "europe", "6"], ["3", "asia", "2"], ["4", "africa", "2"], ["5", "australia", "2"]] | 2,048 | Answer: |
Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"], ["15", "brazil", "1"], ["6", "sweden", "2"], ["2", "germany", "2"], ["3", "france", "2"], ["8", "korea", "3"], ["7", "uk", "2"], ["11", "australia", "5"], ["5", "italy", "2"], ["4", "japan", "3"], ["12", "new zealand", "5"], ["10", "nigeria", "4"], ["14", "mexico", "1"], ["1", "usa", "1"], ["13", "egypt", "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:How many countries are listed? | [["15"]] | 2,048 | Answer: |
Table COUNTRIES: [["CountryId", "CountryName", "Continent"], ["9", "russia", "2"], ["6", "sweden", "2"], ["2", "germany", "2"], ["3", "france", "2"], ["15", "brazil", "1"], ["8", "korea", "3"], ["5", "italy", "2"], ["14", "mexico", "1"], ["7", "uk", "2"], ["4", "japan", "3"], ["11", "australia", "5"], ["1", "usa", "1"], ["12", "new zealand", "5"], ["10", "nigeria", "4"], ["13", "egypt", "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:How many countries exist? | [["15"]] | 2,048 | Answer: |
Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["8", "nissan", "Nissan Motors", "4"], ["13", "daimler benz", "Daimler Benz", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["26", "16", "renault"], ["8", "6", "chrysler"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["23", "15", "peugeot"], ["7", "4", "chevrolet"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["4", "gm", "General Motors", "1"], ["22", "kia", "Kia Motors", "8"], ["16", "renault", "Renault", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["31", "2", "volkswagen"], ["32", "21", "volvo"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["6", "chrysler", "Chrysler", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["20", "8", "nissan"], ["18", "13", "mercedes-benz"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["5", "ford", "Ford Motor Company", "1"], ["21", "volvo", "Volvo", "6"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["29", "19", "toyota"], ["22", "14", "opel"], ["15", "11", "honda"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"], ["2", "volkswagen", "Volkswagen", "2"], ["23", "hyundai", "Hyundai", "8"], ["11", "honda", "Honda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["25", "4", "pontiac"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["18", "subaru", "Subaru", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["3", "3", "bmw"], ["28", "18", "subaru"], ["10", "8", "datsun"], ["2", "2", "audi"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["19", "toyota", "Toyota", "4"], ["3", "bmw", "BMW", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["13", "5", "ford"], ["17", "13", "mercedes"], ["12", "9", "fiat"], ["34", "23", "hyundai"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["33", "22", "kia"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["7", "citroen", "Citroen", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["9", "7", "citroen"], ["24", "6", "plymouth"], ["21", "4", "oldsmobile"], ["35", "6", "jeep"], ["16", "12", "mazda"], ["4", "4", "buick"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["12", "mazda", "Mazda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["5", "4", "cadillac"], ["11", "6", "dodge"], ["1", "1", "amc"], ["27", "17", "saab"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["17", "saab", "Saab", "6"], ["20", "triumph", "Triumph", "7"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["6", "5", "capri"], ["36", "19", "scion"], ["30", "20", "triumph"], ["19", "5", "mercury"]]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 models does each car maker produce? List maker full name, id and the number. | [["American Motor Company", "1", "1"], ["Volkswagen", "2", "2"], ["BMW", "3", "1"], ["General Motors", "4", "5"], ["Ford Motor Company", "5", "3"], ["Chrysler", "6", "4"], ["Citroen", "7", "1"], ["Nissan Motors", "8", "2"], ["Fiat", "9", "1"], ["Honda", "11", "1"], ["Mazda", "12", "1"], ["Daimler Benz", "13", "2"], ["Opel", "14", "1"], ["Peugeaut", "15", "1"], ["Renault", "16", "1"], ["Saab", "17", "1"], ["Subaru", "18", "1"], ["Toyota", "19", "2"], ["Triumph", "20", "1"], ["Volvo", "21", "1"], ["Kia Motors", "22", "1"], ["Hyundai", "23", "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"], ["26", "16", "renault"], ["31", "2", "volkswagen"], ["7", "4", "chevrolet"], ["18", "13", "mercedes-benz"], ["23", "15", "peugeot"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["16", "renault", "Renault", "3"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["20", "8", "nissan"], ["29", "19", "toyota"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["2", "volkswagen", "Volkswagen", "2"], ["23", "hyundai", "Hyundai", "8"], ["19", "toyota", "Toyota", "4"], ["18", "subaru", "Subaru", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["28", "18", "subaru"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["21", "volvo", "Volvo", "6"], ["4", "gm", "General Motors", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["32", "21", "volvo"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["1", "amc", "American Motor Company", "1"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["22", "14", "opel"], ["8", "6", "chrysler"], ["34", "23", "hyundai"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["14", "opel", "Opel", "2"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["3", "3", "bmw"], ["25", "4", "pontiac"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["3", "bmw", "BMW", "2"], ["6", "chrysler", "Chrysler", "1"], ["5", "ford", "Ford Motor Company", "1"]]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"], ["15", "11", "honda"], ["17", "13", "mercedes"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["7", "citroen", "Citroen", "3"], ["11", "honda", "Honda", "4"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["2", "2", "audi"], ["21", "4", "oldsmobile"], ["10", "8", "datsun"], ["12", "9", "fiat"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["9", "fiat", "Fiat", "5"]]Table MODEL_LIST: [["ModelId", "Maker", "Model"], ["35", "6", "jeep"], ["13", "5", "ford"], ["9", "7", "citroen"], ["33", "22", "kia"], ["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"], ["30", "20", "triumph"], ["19", "5", "mercury"]]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 full name of each car maker, along with its id and how many models it produces? | [["American Motor Company", "1", "1"], ["Volkswagen", "2", "2"], ["BMW", "3", "1"], ["General Motors", "4", "5"], ["Ford Motor Company", "5", "3"], ["Chrysler", "6", "4"], ["Citroen", "7", "1"], ["Nissan Motors", "8", "2"], ["Fiat", "9", "1"], ["Honda", "11", "1"], ["Mazda", "12", "1"], ["Daimler Benz", "13", "2"], ["Opel", "14", "1"], ["Peugeaut", "15", "1"], ["Renault", "16", "1"], ["Saab", "17", "1"], ["Subaru", "18", "1"], ["Toyota", "19", "2"], ["Triumph", "20", "1"], ["Volvo", "21", "1"], ["Kia Motors", "22", "1"], ["Hyundai", "23", "1"]] | 2,048 | Answer: |
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["173", "chevrolet", "chevrolet monza 2+2"], ["40", "volkswagen", "volkswagen super beetle 117"], ["18", "ford", "ford mustang boss 302"], ["54", "chevrolet", "chevrolet vega (sw)"], ["334", "volkswagen", "vw dasher (diesel)"], ["180", "volkswagen", "volkswagen dasher"], ["110", "volkswagen", "volkswagen super beetle"], ["216", "plymouth", "plymouth volare premier v8"], ["50", "dodge", "dodge monaco (sw)"], ["203", "chevrolet", "chevrolet chevette"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["335", "audi", "audi 5000s (diesel)"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["286", "volkswagen", "volkswagen scirocco"], ["63", "volkswagen", "volkswagen model 111"], ["244", "ford", "ford mustang ii 2+2"], ["174", "ford", "ford mustang ii"], ["82", "ford", "ford gran torino (sw)"], ["117", "chevrolet", "chevrolet vega"], ["69", "ford", "ford pinto runabout"], ["245", "chevrolet", "chevrolet chevette"], ["355", "datsun", "datsun 210 mpg"], ["161", "chevrolet", "chevrolet nova"], ["198", "ford", "ford gran torino"], ["87", "renault", "renault 12 (sw)"], ["136", "chevrolet", "chevrolet nova"], ["340", " volkswagen", "volkswagen rabbit"], ["13", "ford", "ford torino (sw)"], ["319", "chevrolet", "chevrolet chevette"], ["356", "toyota", "toyota tercel"], ["200", "chevrolet", "chevrolet nova"], ["147", "ford", "ford gran torino (sw)"], ["248", "volkswagen", "volkswagen dasher"], ["46", "chevrolet", "chevrolet impala"], ["376", "chevrolet", "chevrolet cavalier"], ["111", "chevrolet", "chevrolet impala"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["44", "ford", "ford torino 500"], ["344", "ford", "ford mustang cobra"], ["182", "ford", "ford pinto"], ["183", "volkswagen", "volkswagen rabbit"], ["11", "citroen", "citroen ds-21 pallas"], ["165", "chevrolet", "chevrolet bel air"], ["88", "ford", "ford pinto (sw)"], ["101", "plymouth", "plymouth fury gran sedan"], ["59", "peugeot", "peugeot 304"], ["150", "volkswagen", "volkswagen dasher"], ["19", "chevrolet", "chevrolet monte carlo"], ["37", "chevrolet", "chevrolet vega 2300"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["211", "volkswagen", "volkswagen rabbit"], ["20", "buick", "buick estate wagon (sw)"], ["384", "volkswagen", "volkswagen rabbit l"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["233", "chevrolet", "chevrolet concours"], ["231", "dodge", "dodge monaco brougham"], ["293", "chevrolet", "chevrolet caprice classic"], ["359", "ford", "ford escort 4w"], ["338", "renault", "renault lecar deluxe"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["229", "chevrolet", "chevrolet caprice classic"], ["96", "ford", "ford gran torino"], ["194", "renault", "renault 12tl"], ["144", "ford", "ford gran torino"], ["273", "dodge", "dodge magnum xe"], ["205", "volkswagen", "vw rabbit"], ["371", "datsun", "datsun 810 maxima"], ["285", "peugeot", "peugeot 604sl"], ["360", "ford", "ford escort 2h"], ["138", "ford", "ford pinto"], ["362", "renault", "renault 18i"], ["7", "chevrolet", "chevrolet impala"], ["274", "chevrolet", "chevrolet chevette"], ["67", "volkswagen", "volkswagen type 3"], ["325", "audi", "audi 4000"], ["191", "opel", "opel 1900"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["16", "dodge", "dodge challenger se"], ["62", "datsun", "datsun 1200"], ["204", "chevrolet", "chevrolet woody"], ["317", "volkswagen", "vw rabbit"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["217", "peugeot", "peugeot 504"], ["68", "chevrolet", "chevrolet vega"], ["39", "ford", "ford pinto"], ["48", "ford", "ford galaxie 500"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["89", "datsun", "datsun 510 (sw)"], ["382", "ford", "ford fairmont futura"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["214", "ford", "ford pinto"], ["226", "renault", "renault 5 gtl"], ["99", "chevrolet", "chevrolet caprice classic"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["169", "chevrolet", "chevrolet chevelle malibu"], ["272", "ford", "ford futura"], ["239", "chrysler", "chrysler cordoba"], ["241", "volkswagen", "volkswagen rabbit custom"], ["123", "chevrolet", "chevrolet monte carlo s"], ["249", "datsun", "datsun 810"], ["27", "peugeot", "peugeot 504"], ["370", "toyota", "toyota cressida"], ["56", "ford", "ford mustang"], ["5", "ford", "ford torino"], ["212", "datsun", "datsun b-210"], ["58", "opel", "opel 1900"], ["271", "buick", "buick regal sport coupe (turbo)"], ["225", "buick", "buick opel isuzu deluxe"], ["120", "ford", "ford pinto"], ["176", "ford", "ford pinto"], ["185", "audi", "audi 100ls"], ["207", "dodge", "dodge aspen se"], ["186", "peugeot", "peugeot 504"], ["70", "chevrolet", "chevrolet impala"], ["196", "dodge", "dodge coronet brougham"], ["380", "dodge", "dodge aries se"], ["43", "chevrolet", "chevrolet chevelle malibu"], ["328", "datsun", "datsun 510 hatchback"], ["361", "volkswagen", "volkswagen jetta"], ["193", "dodge", "dodge colt"], ["36", "datsun", "datsun pl510"], ["34", "dodge", "dodge d200"], ["118", "datsun", "datsun 610"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["363", "honda", "honda prelude"], ["277", "dodge", "dodge omni"], ["307", "peugeot", "peugeot 504"], ["402", "ford", "ford mustang gl"], ["393", "honda", "honda civic (auto)"], ["276", "datsun", "datsun 510"], ["406", "chevrolet", "chevy s-10"], ["208", "ford", "ford granada ghia"], ["86", "peugeot", "peugeot 504 (sw)"], ["51", "ford", "ford country squire (sw)"], ["181", "datsun", "datsun 710"], ["33", "chevrolet", "chevy c20"], ["343", "triumph", "triumph tr7 coupe"], ["25", "datsun", "datsun pl510"], ["73", "ford", "ford galaxie 500"], ["189", "honda", "honda civic cvcc"]] | 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 of the car has the minimum horsepower? | [["amc"]] | 2,048 | Answer: |
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["173", "chevrolet", "chevrolet monza 2+2"], ["54", "chevrolet", "chevrolet vega (sw)"], ["376", "chevrolet", "chevrolet cavalier"], ["180", "volkswagen", "volkswagen dasher"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["40", "volkswagen", "volkswagen super beetle 117"], ["69", "ford", "ford pinto runabout"], ["286", "volkswagen", "volkswagen scirocco"], ["117", "chevrolet", "chevrolet vega"], ["245", "chevrolet", "chevrolet chevette"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["319", "chevrolet", "chevrolet chevette"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["335", "audi", "audi 5000s (diesel)"], ["200", "chevrolet", "chevrolet nova"], ["198", "ford", "ford gran torino"], ["203", "chevrolet", "chevrolet chevette"], ["110", "volkswagen", "volkswagen super beetle"], ["82", "ford", "ford gran torino (sw)"], ["111", "chevrolet", "chevrolet impala"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["384", "volkswagen", "volkswagen rabbit l"], ["334", "volkswagen", "vw dasher (diesel)"], ["356", "toyota", "toyota tercel"], ["147", "ford", "ford gran torino (sw)"], ["50", "dodge", "dodge monaco (sw)"], ["136", "chevrolet", "chevrolet nova"], ["87", "renault", "renault 12 (sw)"], ["46", "chevrolet", "chevrolet impala"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["37", "chevrolet", "chevrolet vega 2300"], ["248", "volkswagen", "volkswagen dasher"], ["355", "datsun", "datsun 210 mpg"], ["183", "volkswagen", "volkswagen rabbit"], ["285", "peugeot", "peugeot 604sl"], ["174", "ford", "ford mustang ii"], ["13", "ford", "ford torino (sw)"], ["96", "ford", "ford gran torino"], ["88", "ford", "ford pinto (sw)"], ["67", "volkswagen", "volkswagen type 3"], ["340", " volkswagen", "volkswagen rabbit"], ["229", "chevrolet", "chevrolet caprice classic"], ["182", "ford", "ford pinto"], ["233", "chevrolet", "chevrolet concours"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["101", "plymouth", "plymouth fury gran sedan"], ["59", "peugeot", "peugeot 304"], ["150", "volkswagen", "volkswagen dasher"], ["7", "chevrolet", "chevrolet impala"], ["338", "renault", "renault lecar deluxe"], ["44", "ford", "ford torino 500"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["5", "ford", "ford torino"], ["211", "volkswagen", "volkswagen rabbit"], ["99", "chevrolet", "chevrolet caprice classic"], ["138", "ford", "ford pinto"], ["63", "volkswagen", "volkswagen model 111"], ["68", "chevrolet", "chevrolet vega"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["123", "chevrolet", "chevrolet monte carlo s"], ["293", "chevrolet", "chevrolet caprice classic"], ["165", "chevrolet", "chevrolet bel air"], ["18", "ford", "ford mustang boss 302"], ["359", "ford", "ford escort 4w"], ["144", "ford", "ford gran torino"], ["178", "pontiac", "pontiac astro"], ["244", "ford", "ford mustang ii 2+2"], ["205", "volkswagen", "vw rabbit"], ["19", "chevrolet", "chevrolet monte carlo"], ["217", "peugeot", "peugeot 504"], ["317", "volkswagen", "vw rabbit"], ["161", "chevrolet", "chevrolet nova"], ["204", "chevrolet", "chevrolet woody"], ["11", "citroen", "citroen ds-21 pallas"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["70", "chevrolet", "chevrolet impala"], ["216", "plymouth", "plymouth volare premier v8"], ["194", "renault", "renault 12tl"], ["273", "dodge", "dodge magnum xe"], ["208", "ford", "ford granada ghia"], ["185", "audi", "audi 100ls"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["27", "peugeot", "peugeot 504"], ["20", "buick", "buick estate wagon (sw)"], ["239", "chrysler", "chrysler cordoba"], ["226", "renault", "renault 5 gtl"], ["241", "volkswagen", "volkswagen rabbit custom"], ["186", "peugeot", "peugeot 504"], ["382", "ford", "ford fairmont futura"], ["176", "ford", "ford pinto"], ["371", "datsun", "datsun 810 maxima"], ["362", "renault", "renault 18i"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["62", "datsun", "datsun 1200"], ["89", "datsun", "datsun 510 (sw)"], ["360", "ford", "ford escort 2h"], ["120", "ford", "ford pinto"], ["60", "fiat", "fiat 124b"], ["207", "dodge", "dodge aspen se"], ["344", "ford", "ford mustang cobra"], ["328", "datsun", "datsun 510 hatchback"], ["274", "chevrolet", "chevrolet chevette"], ["374", "ford", "ford granada gl"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["169", "chevrolet", "chevrolet chevelle malibu"], ["48", "ford", "ford galaxie 500"], ["214", "ford", "ford pinto"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["39", "ford", "ford pinto"], ["140", "chevrolet", "chevrolet vega"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["325", "audi", "audi 4000"], ["236", "ford", "ford granada"], ["95", "chevrolet", "chevrolet malibu"], ["86", "peugeot", "peugeot 504 (sw)"], ["307", "peugeot", "peugeot 504"], ["272", "ford", "ford futura"], ["380", "dodge", "dodge aries se"], ["36", "datsun", "datsun pl510"], ["406", "chevrolet", "chevy s-10"], ["276", "datsun", "datsun 510"], ["249", "datsun", "datsun 810"], ["231", "dodge", "dodge monaco brougham"], ["370", "toyota", "toyota cressida"], ["56", "ford", "ford mustang"], ["225", "buick", "buick opel isuzu deluxe"], ["122", "fiat", "fiat 124 sport coupe"], ["25", "datsun", "datsun pl510"], ["127", "audi", "audi 100ls"], ["275", "toyota", "toyota corona"], ["126", "opel", "opel manta"], ["402", "ford", "ford mustang gl"], ["361", "volkswagen", "volkswagen jetta"], ["191", "opel", "opel 1900"], ["118", "datsun", "datsun 610"], ["106", "chevrolet", "chevrolet nova custom"], ["16", "dodge", "dodge challenger se"], ["33", "chevrolet", "chevy c20"], ["190", "fiat", "fiat 131"], ["316", "pontiac", "pontiac phoenix"], ["318", "toyota", "toyota corolla tercel"], ["58", "opel", "opel 1900"], ["193", "dodge", "dodge colt"], ["343", "triumph", "triumph tr7 coupe"]] | 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 model of the car with the smallest amount of horsepower? | [["amc"]] | 2,048 | Answer: |
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["50", "dodge", "dodge monaco (sw)"], ["231", "dodge", "dodge monaco brougham"], ["180", "volkswagen", "volkswagen dasher"], ["67", "volkswagen", "volkswagen type 3"], ["40", "volkswagen", "volkswagen super beetle 117"], ["110", "volkswagen", "volkswagen super beetle"], ["173", "chevrolet", "chevrolet monza 2+2"], ["87", "renault", "renault 12 (sw)"], ["340", " volkswagen", "volkswagen rabbit"], ["362", "renault", "renault 18i"], ["286", "volkswagen", "volkswagen scirocco"], ["63", "volkswagen", "volkswagen model 111"], ["307", "peugeot", "peugeot 504"], ["384", "volkswagen", "volkswagen rabbit l"], ["183", "volkswagen", "volkswagen rabbit"], ["150", "volkswagen", "volkswagen dasher"], ["229", "chevrolet", "chevrolet caprice classic"], ["186", "peugeot", "peugeot 504"], ["241", "volkswagen", "volkswagen rabbit custom"], ["277", "dodge", "dodge omni"], ["82", "ford", "ford gran torino (sw)"], ["59", "peugeot", "peugeot 304"], ["211", "volkswagen", "volkswagen rabbit"], ["258", "dodge", "dodge diplomat"], ["145", "buick", "buick century luxus (sw)"], ["293", "chevrolet", "chevrolet caprice classic"], ["158", "subaru", "subaru"], ["334", "volkswagen", "vw dasher (diesel)"], ["245", "chevrolet", "chevrolet chevette"], ["27", "peugeot", "peugeot 504"], ["248", "volkswagen", "volkswagen dasher"], ["376", "chevrolet", "chevrolet cavalier"], ["338", "renault", "renault lecar deluxe"], ["356", "toyota", "toyota tercel"], ["354", "subaru", "subaru"], ["99", "chevrolet", "chevrolet caprice classic"], ["147", "ford", "ford gran torino (sw)"], ["278", "toyota", "toyota celica gt liftback"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["370", "toyota", "toyota cressida"], ["196", "dodge", "dodge coronet brougham"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["317", "volkswagen", "vw rabbit"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["217", "peugeot", "peugeot 504"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["116", "toyota", "toyota carina"], ["239", "chrysler", "chrysler cordoba"], ["285", "peugeot", "peugeot 604sl"], ["275", "toyota", "toyota corona"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["45", "amc", "amc matador"], ["193", "dodge", "dodge colt"], ["86", "peugeot", "peugeot 504 (sw)"], ["170", "amc", "amc matador"], ["403", "volkswagen", "vw pickup"], ["34", "dodge", "dodge d200"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["84", "volvo", "volvo 145e (sw)"], ["185", "audi", "audi 100ls"], ["326", "toyota", "toyota corona liftback"], ["194", "renault", "renault 12tl"], ["325", "audi", "audi 4000"], ["13", "ford", "ford torino (sw)"], ["363", "honda", "honda prelude"], ["148", "amc", "amc matador (sw)"], ["122", "fiat", "fiat 124 sport coupe"], ["382", "ford", "ford fairmont futura"], ["144", "ford", "ford gran torino"], ["205", "volkswagen", "vw rabbit"], ["96", "ford", "ford gran torino"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["90", "toyota", "toyota corona mark ii (sw)"], ["262", "ford", "ford fairmont (auto)"], ["321", "chevrolet", "chevrolet citation"], ["380", "dodge", "dodge aries se"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["179", "toyota", "toyota corona"], ["247", "subaru", "subaru dl"], ["44", "ford", "ford torino 500"], ["197", "amc", "amc matador"], ["154", "dodge", "dodge colt"], ["161", "chevrolet", "chevrolet nova"], ["331", "dodge", "dodge colt"], ["38", "toyota", "toyota corona"], ["233", "chevrolet", "chevrolet concours"], ["244", "ford", "ford mustang ii 2+2"], ["91", "dodge", "dodge colt (sw)"], ["246", "dodge", "dodge colt m/m"], ["361", "volkswagen", "volkswagen jetta"], ["33", "chevrolet", "chevy c20"], ["360", "ford", "ford escort 2h"], ["324", "dodge", "dodge aspen"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["131", "toyota", "toyota mark ii"], ["273", "dodge", "dodge magnum xe"], ["234", "buick", "buick skylark"], ["46", "chevrolet", "chevrolet impala"], ["21", "toyota", "toyota corona mark ii"], ["19", "chevrolet", "chevrolet monte carlo"], ["372", "buick", "buick century"], ["16", "dodge", "dodge challenger se"], ["301", "volkswagen", "vw rabbit custom"], ["97", "dodge", "dodge coronet custom"], ["126", "opel", "opel manta"], ["263", "ford", "ford fairmont (man)"], ["111", "chevrolet", "chevrolet impala"], ["268", "dodge", "dodge aspen"], ["123", "chevrolet", "chevrolet monte carlo s"], ["318", "toyota", "toyota corolla tercel"], ["314", "chevrolet", "chevrolet citation"], ["218", "toyota", "toyota mark ii"], ["152", "toyota", "toyota corona"], ["146", "dodge", "dodge coronet custom (sw)"], ["80", "amc", "amc matador (sw)"], ["190", "fiat", "fiat 131"], ["136", "chevrolet", "chevrolet nova"], ["349", "chevrolet", "chevrolet citation"], ["88", "ford", "ford pinto (sw)"], ["393", "honda", "honda civic (auto)"], ["2", "buick", "buick skylark 320"], ["117", "chevrolet", "chevrolet vega"], ["200", "chevrolet", "chevrolet nova"], ["174", "ford", "ford mustang ii"], ["30", "bmw", "bmw 2002"], ["347", "buick", "buick skylark"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["54", "chevrolet", "chevrolet vega (sw)"], ["225", "buick", "buick opel isuzu deluxe"], ["101", "plymouth", "plymouth fury gran sedan"], ["69", "ford", "ford pinto runabout"], ["215", "volvo", "volvo 245"], ["319", "chevrolet", "chevrolet chevette"], ["177", "amc", "amc gremlin"], ["18", "ford", "ford mustang boss 302"], ["165", "chevrolet", "chevrolet bel air"], ["207", "dodge", "dodge aspen se"]]Table CARS_DATA: [["Id", "MPG", "Cylinders", "Edispl", "Horsepower", "Weight", "Accelerate", "Year"], ["44", "19", "6", "250.0", "88", "3302", "15.5", "1971"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["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:Find the model of the car whose weight is below the average weight. | [["toyota"], ["plymouth"], ["amc"], ["ford"], ["datsun"], ["volkswagen"], ["peugeot"], ["audi"], ["saab"], ["bmw"], ["amc"], ["datsun"], ["chevrolet"], ["toyota"], ["ford"], ["volkswagen"], ["amc"], ["amc"], ["chevrolet"], ["mercury"], ["opel"], ["peugeot"], ["fiat"], ["toyota"], ["datsun"], ["volkswagen"], ["plymouth"], ["toyota"], ["dodge"], ["volkswagen"], ["chevrolet"], ["ford"], ["mazda"], ["volvo"], ["volkswagen"], ["peugeot"], ["renault"], ["ford"], ["datsun"], ["toyota"], ["dodge"], ["toyota"], ["amc"], ["plymouth"], ["volkswagen"], ["amc"], ["toyota"], ["chevrolet"], ["datsun"], ["mazda"], ["ford"], ["mercury"], ["fiat"], ["fiat"], ["opel"], ["audi"], ["volvo"], ["saab"], ["toyota"], ["ford"], ["amc"], ["datsun"], ["ford"], ["toyota"], ["chevrolet"], ["audi"], ["volkswagen"], ["opel"], ["toyota"], ["datsun"], ["dodge"], ["fiat"], ["fiat"], ["honda"], ["subaru"], ["fiat"], ["toyota"], ["ford"], ["amc"], ["pontiac"], ["toyota"], ["volkswagen"], ["datsun"], ["volkswagen"], ["audi"], ["peugeot"], ["volvo"], ["saab"], ["honda"], ["fiat"], ["opel"], ["capri"], ["dodge"], ["renault"], ["chevrolet"], ["chevrolet"], ["volkswagen"], ["honda"], ["volkswagen"], ["datsun"], ["toyota"], ["ford"], ["toyota"], ["honda"], ["buick"], ["renault"], ["plymouth"], ["datsun"], ["volkswagen"], ["pontiac"], ["toyota"], ["ford"], ["chevrolet"], ["dodge"], ["subaru"], ["volkswagen"], ["datsun"], ["bmw"], ["mazda"], ["volkswagen"], ["ford"], ["mazda"], ["datsun"], ["honda"], ["ford"], ["ford"], ["chevrolet"], ["toyota"], ["datsun"], ["dodge"], ["toyota"], ["plymouth"], ["oldsmobile"], ["datsun"], ["audi"], ["saab"], ["volkswagen"], ["honda"], ["ford"], ["volkswagen"], ["mazda"], ["dodge"], ["amc"], ["plymouth"], ["plymouth"], ["datsun"], ["fiat"], ["buick"], ["chevrolet"], ["oldsmobile"], ["pontiac"], ["volkswagen"], ["toyota"], ["chevrolet"], ["datsun"], ["chevrolet"], ["ford"], ["audi"], ["toyota"], ["mazda"], ["datsun"], ["toyota"], ["mazda"], ["dodge"], ["datsun"], ["volkswagen"], ["volkswagen"], ["audi"], ["honda"], ["renault"], ["subaru"], [" volkswagen"], ["datsun"], ["mazda"], ["triumph"], ["ford"], ["honda"], ["plymouth"], ["buick"], ["dodge"], ["chevrolet"], ["plymouth"], ["toyota"], ["plymouth"], ["honda"], ["subaru"], ["datsun"], ["toyota"], ["mazda"], ["plymouth"], ["ford"], ["ford"], ["volkswagen"], ["renault"], ["honda"], ["toyota"], ["datsun"], ["mazda"], ["saab"], ["toyota"], ["datsun"], ["chevrolet"], ["chevrolet"], ["chevrolet"], ["pontiac"], ["dodge"], ["pontiac"], ["ford"], ["volkswagen"], ["mazda"], ["mazda"], ["plymouth"], ["mercury"], ["nissan"], ["honda"], ["toyota"], ["honda"], ["honda"], ["datsun"], ["buick"], ["chrysler"], ["ford"], ["toyota"], ["dodge"], ["chevrolet"], ["ford"], ["volkswagen"], ["dodge"], ["ford"], ["chevrolet"]] | 2,048 | Answer: |
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["26", "volkswagen", "volkswagen 1131 deluxe sedan"], ["376", "chevrolet", "chevrolet cavalier"], ["229", "chevrolet", "chevrolet caprice classic"], ["173", "chevrolet", "chevrolet monza 2+2"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["40", "volkswagen", "volkswagen super beetle 117"], ["50", "dodge", "dodge monaco (sw)"], ["67", "volkswagen", "volkswagen type 3"], ["180", "volkswagen", "volkswagen dasher"], ["286", "volkswagen", "volkswagen scirocco"], ["293", "chevrolet", "chevrolet caprice classic"], ["136", "chevrolet", "chevrolet nova"], ["110", "volkswagen", "volkswagen super beetle"], ["87", "renault", "renault 12 (sw)"], ["63", "volkswagen", "volkswagen model 111"], ["356", "toyota", "toyota tercel"], ["200", "chevrolet", "chevrolet nova"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["384", "volkswagen", "volkswagen rabbit l"], ["340", " volkswagen", "volkswagen rabbit"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["99", "chevrolet", "chevrolet caprice classic"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["241", "volkswagen", "volkswagen rabbit custom"], ["338", "renault", "renault lecar deluxe"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["278", "toyota", "toyota celica gt liftback"], ["245", "chevrolet", "chevrolet chevette"], ["106", "chevrolet", "chevrolet nova custom"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["19", "chevrolet", "chevrolet monte carlo"], ["334", "volkswagen", "vw dasher (diesel)"], ["183", "volkswagen", "volkswagen rabbit"], ["161", "chevrolet", "chevrolet nova"], ["326", "toyota", "toyota corona liftback"], ["150", "volkswagen", "volkswagen dasher"], ["46", "chevrolet", "chevrolet impala"], ["165", "chevrolet", "chevrolet bel air"], ["231", "dodge", "dodge monaco brougham"], ["233", "chevrolet", "chevrolet concours"], ["54", "chevrolet", "chevrolet vega (sw)"], ["123", "chevrolet", "chevrolet monte carlo s"], ["7", "chevrolet", "chevrolet impala"], ["248", "volkswagen", "volkswagen dasher"], ["275", "toyota", "toyota corona"], ["145", "buick", "buick century luxus (sw)"], ["370", "toyota", "toyota cressida"], ["285", "peugeot", "peugeot 604sl"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["111", "chevrolet", "chevrolet impala"], ["13", "ford", "ford torino (sw)"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["328", "datsun", "datsun 510 hatchback"], ["82", "ford", "ford gran torino (sw)"], ["59", "peugeot", "peugeot 304"], ["318", "toyota", "toyota corolla tercel"], ["122", "fiat", "fiat 124 sport coupe"], ["147", "ford", "ford gran torino (sw)"], ["239", "chrysler", "chrysler cordoba"], ["362", "renault", "renault 18i"], ["116", "toyota", "toyota carina"], ["317", "volkswagen", "vw rabbit"], ["178", "pontiac", "pontiac astro"], ["117", "chevrolet", "chevrolet vega"], ["207", "dodge", "dodge aspen se"], ["252", "volkswagen", "volkswagen rabbit custom diesel"], ["186", "peugeot", "peugeot 504"], ["336", "mercedes-benz", "mercedes-benz 240d"], ["225", "buick", "buick opel isuzu deluxe"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["211", "volkswagen", "volkswagen rabbit"], ["101", "plymouth", "plymouth fury gran sedan"], ["169", "chevrolet", "chevrolet chevelle malibu"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["20", "buick", "buick estate wagon (sw)"], ["85", "volkswagen", "volkswagen 411 (sw)"], ["37", "chevrolet", "chevrolet vega 2300"], ["88", "ford", "ford pinto (sw)"], ["38", "toyota", "toyota corona"], ["194", "renault", "renault 12tl"], ["69", "ford", "ford pinto runabout"], ["335", "audi", "audi 5000s (diesel)"], ["217", "peugeot", "peugeot 504"], ["307", "peugeot", "peugeot 504"], ["62", "datsun", "datsun 1200"], ["319", "chevrolet", "chevrolet chevette"], ["243", "toyota", "toyota corolla liftback"], ["84", "volvo", "volvo 145e (sw)"], ["355", "datsun", "datsun 210 mpg"], ["363", "honda", "honda prelude"], ["33", "chevrolet", "chevy c20"], ["86", "peugeot", "peugeot 504 (sw)"], ["179", "toyota", "toyota corona"], ["185", "audi", "audi 100ls"], ["27", "peugeot", "peugeot 504"], ["121", "mercury", "mercury capri v6"], ["216", "plymouth", "plymouth volare premier v8"], ["205", "volkswagen", "vw rabbit"], ["361", "volkswagen", "volkswagen jetta"], ["70", "chevrolet", "chevrolet impala"], ["292", "dodge", "dodge aspen 6"], ["343", "triumph", "triumph tr7 coupe"], ["43", "chevrolet", "chevrolet chevelle malibu"], ["141", "chevrolet", "chevrolet chevelle malibu classic"], ["52", "pontiac", "pontiac safari (sw)"], ["203", "chevrolet", "chevrolet chevette"], ["367", "peugeot", "peugeot 505s turbo diesel"], ["2", "buick", "buick skylark 320"], ["90", "toyota", "toyota corona mark ii (sw)"], ["371", "datsun", "datsun 810 maxima"], ["273", "dodge", "dodge magnum xe"], ["181", "datsun", "datsun 710"], ["96", "ford", "ford gran torino"], ["271", "buick", "buick regal sport coupe (turbo)"], ["44", "ford", "ford torino 500"], ["146", "dodge", "dodge coronet custom (sw)"], ["268", "dodge", "dodge aspen"], ["380", "dodge", "dodge aries se"], ["140", "chevrolet", "chevrolet vega"], ["36", "datsun", "datsun pl510"], ["234", "buick", "buick skylark"], ["297", "buick", "buick estate wagon (sw)"], ["299", "chevrolet", "chevrolet malibu classic (sw)"], ["21", "toyota", "toyota corona mark ii"], ["16", "dodge", "dodge challenger se"], ["226", "renault", "renault 5 gtl"], ["118", "datsun", "datsun 610"], ["60", "fiat", "fiat 124b"], ["152", "toyota", "toyota corona"], ["406", "chevrolet", "chevy s-10"], ["288", "pontiac", "pontiac lemans v6"], ["218", "toyota", "toyota mark ii"], ["262", "ford", "ford fairmont (auto)"], ["68", "chevrolet", "chevrolet vega"], ["347", "buick", "buick skylark"], ["164", "pontiac", "pontiac catalina"], ["124", "pontiac", "pontiac grand prix"], ["89", "datsun", "datsun 510 (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 model for the car with a weight smaller than the average? | [["toyota"], ["plymouth"], ["amc"], ["ford"], ["datsun"], ["volkswagen"], ["peugeot"], ["audi"], ["saab"], ["bmw"], ["amc"], ["datsun"], ["chevrolet"], ["toyota"], ["ford"], ["volkswagen"], ["amc"], ["amc"], ["chevrolet"], ["mercury"], ["opel"], ["peugeot"], ["fiat"], ["toyota"], ["datsun"], ["volkswagen"], ["plymouth"], ["toyota"], ["dodge"], ["volkswagen"], ["chevrolet"], ["ford"], ["mazda"], ["volvo"], ["volkswagen"], ["peugeot"], ["renault"], ["ford"], ["datsun"], ["toyota"], ["dodge"], ["toyota"], ["amc"], ["plymouth"], ["volkswagen"], ["amc"], ["toyota"], ["chevrolet"], ["datsun"], ["mazda"], ["ford"], ["mercury"], ["fiat"], ["fiat"], ["opel"], ["audi"], ["volvo"], ["saab"], ["toyota"], ["ford"], ["amc"], ["datsun"], ["ford"], ["toyota"], ["chevrolet"], ["audi"], ["volkswagen"], ["opel"], ["toyota"], ["datsun"], ["dodge"], ["fiat"], ["fiat"], ["honda"], ["subaru"], ["fiat"], ["toyota"], ["ford"], ["amc"], ["pontiac"], ["toyota"], ["volkswagen"], ["datsun"], ["volkswagen"], ["audi"], ["peugeot"], ["volvo"], ["saab"], ["honda"], ["fiat"], ["opel"], ["capri"], ["dodge"], ["renault"], ["chevrolet"], ["chevrolet"], ["volkswagen"], ["honda"], ["volkswagen"], ["datsun"], ["toyota"], ["ford"], ["toyota"], ["honda"], ["buick"], ["renault"], ["plymouth"], ["datsun"], ["volkswagen"], ["pontiac"], ["toyota"], ["ford"], ["chevrolet"], ["dodge"], ["subaru"], ["volkswagen"], ["datsun"], ["bmw"], ["mazda"], ["volkswagen"], ["ford"], ["mazda"], ["datsun"], ["honda"], ["ford"], ["ford"], ["chevrolet"], ["toyota"], ["datsun"], ["dodge"], ["toyota"], ["plymouth"], ["oldsmobile"], ["datsun"], ["audi"], ["saab"], ["volkswagen"], ["honda"], ["ford"], ["volkswagen"], ["mazda"], ["dodge"], ["amc"], ["plymouth"], ["plymouth"], ["datsun"], ["fiat"], ["buick"], ["chevrolet"], ["oldsmobile"], ["pontiac"], ["volkswagen"], ["toyota"], ["chevrolet"], ["datsun"], ["chevrolet"], ["ford"], ["audi"], ["toyota"], ["mazda"], ["datsun"], ["toyota"], ["mazda"], ["dodge"], ["datsun"], ["volkswagen"], ["volkswagen"], ["audi"], ["honda"], ["renault"], ["subaru"], [" volkswagen"], ["datsun"], ["mazda"], ["triumph"], ["ford"], ["honda"], ["plymouth"], ["buick"], ["dodge"], ["chevrolet"], ["plymouth"], ["toyota"], ["plymouth"], ["honda"], ["subaru"], ["datsun"], ["toyota"], ["mazda"], ["plymouth"], ["ford"], ["ford"], ["volkswagen"], ["renault"], ["honda"], ["toyota"], ["datsun"], ["mazda"], ["saab"], ["toyota"], ["datsun"], ["chevrolet"], ["chevrolet"], ["chevrolet"], ["pontiac"], ["dodge"], ["pontiac"], ["ford"], ["volkswagen"], ["mazda"], ["mazda"], ["plymouth"], ["mercury"], ["nissan"], ["honda"], ["toyota"], ["honda"], ["honda"], ["datsun"], ["buick"], ["chrysler"], ["ford"], ["toyota"], ["dodge"], ["chevrolet"], ["ford"], ["volkswagen"], ["dodge"], ["ford"], ["chevrolet"]] | 2,048 | Answer: |
Table CAR_NAMES: [["MakeId", "Model", "Make"], ["70", "chevrolet", "chevrolet impala"], ["377", "chevrolet", "chevrolet cavalier wagon"], ["376", "chevrolet", "chevrolet cavalier"], ["99", "chevrolet", "chevrolet caprice classic"], ["67", "volkswagen", "volkswagen type 3"], ["69", "ford", "ford pinto runabout"], ["147", "ford", "ford gran torino (sw)"], ["238", "chevrolet", "chevrolet monte carlo landau"], ["140", "chevrolet", "chevrolet vega"], ["270", "chevrolet", "chevrolet monte carlo landau"], ["233", "chevrolet", "chevrolet concours"], ["141", "chevrolet", "chevrolet chevelle malibu classic"], ["37", "chevrolet", "chevrolet vega 2300"], ["71", "pontiac", "pontiac catalina"], ["150", "volkswagen", "volkswagen dasher"], ["78", "chrysler", "chrysler newport royal"], ["286", "volkswagen", "volkswagen scirocco"], ["59", "peugeot", "peugeot 304"], ["153", "datsun", "datsun 710"], ["229", "chevrolet", "chevrolet caprice classic"], ["200", "chevrolet", "chevrolet nova"], ["87", "renault", "renault 12 (sw)"], ["82", "ford", "ford gran torino (sw)"], ["68", "chevrolet", "chevrolet vega"], ["194", "renault", "renault 12tl"], ["7", "chevrolet", "chevrolet impala"], ["39", "ford", "ford pinto"], ["281", "datsun", "datsun 200-sx"], ["293", "chevrolet", "chevrolet caprice classic"], ["374", "ford", "ford granada gl"], ["338", "renault", "renault lecar deluxe"], ["12", "chevrolet", "chevrolet chevelle concours (sw)"], ["54", "chevrolet", "chevrolet vega (sw)"], ["33", "chevrolet", "chevy c20"], ["186", "peugeot", "peugeot 504"], ["209", "pontiac", "pontiac ventura sj"], ["379", "pontiac", "pontiac j2000 se hatchback"], ["319", "chevrolet", "chevrolet chevette"], ["401", "chevrolet", "chevrolet camaro"], ["65", "toyota", "toyota corona hardtop"], ["86", "peugeot", "peugeot 504 (sw)"], ["144", "ford", "ford gran torino"], ["381", "pontiac", "pontiac phoenix"], ["356", "toyota", "toyota tercel"], ["372", "buick", "buick century"], ["81", "chevrolet", "chevrolet chevelle concours (sw)"], ["88", "ford", "ford pinto (sw)"], ["333", "volkswagen", "vw rabbit c (diesel)"], ["58", "opel", "opel 1900"], ["73", "ford", "ford galaxie 500"], ["317", "volkswagen", "vw rabbit"], ["111", "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"], ["334", "volkswagen", "vw dasher (diesel)"], ["96", "ford", "ford gran torino"], ["275", "toyota", "toyota corona"], ["198", "ford", "ford gran torino"], ["399", "toyota", "toyota celica gt"], ["169", "chevrolet", "chevrolet chevelle malibu"], ["180", "volkswagen", "volkswagen dasher"], ["27", "peugeot", "peugeot 504"], ["382", "ford", "ford fairmont futura"], ["262", "ford", "ford fairmont (auto)"], ["106", "chevrolet", "chevrolet nova custom"], ["307", "peugeot", "peugeot 504"], ["47", "pontiac", "pontiac catalina brougham"], ["378", "chevrolet", "chevrolet cavalier 2-door"], ["181", "datsun", "datsun 710"], ["396", "oldsmobile", "oldsmobile cutlass ciera (diesel)"], ["195", "chevrolet", "chevrolet chevelle malibu classic"], ["38", "toyota", "toyota corona"], ["30", "bmw", "bmw 2002"], ["13", "ford", "ford torino (sw)"], ["173", "chevrolet", "chevrolet monza 2+2"], ["205", "volkswagen", "vw rabbit"], ["46", "chevrolet", "chevrolet impala"], ["371", "datsun", "datsun 810 maxima"], ["185", "audi", "audi 100ls"], ["178", "pontiac", "pontiac astro"], ["285", "peugeot", "peugeot 604sl"], ["397", "chrysler", "chrysler lebaron medallion"], ["406", "chevrolet", "chevy s-10"], ["219", "mercedes-benz", "mercedes-benz 280s"], ["250", "bmw", "bmw 320i"], ["237", "pontiac", "pontiac grand prix lj"], ["90", "toyota", "toyota corona mark ii (sw)"], ["373", "oldsmobile", "oldsmobile cutlass ls"], ["110", "volkswagen", "volkswagen super beetle"], ["60", "fiat", "fiat 124b"], ["221", "chevrolet", "chevy c10"], ["239", "chrysler", "chrysler cordoba"], ["95", "chevrolet", "chevrolet malibu"], ["40", "volkswagen", "volkswagen super beetle 117"], ["136", "chevrolet", "chevrolet nova"], ["19", "chevrolet", "chevrolet monte carlo"], ["131", "toyota", "toyota mark ii"], ["203", "chevrolet", "chevrolet chevette"], ["29", "saab", "saab 99e"], ["120", "ford", "ford pinto"], ["1", "chevrolet", "chevrolet chevelle malibu"], ["272", "ford", "ford futura"], ["260", "pontiac", "pontiac phoenix lj"], ["384", "volkswagen", "volkswagen rabbit l"], ["139", "toyota", "toyota corolla 1200"], ["393", "honda", "honda civic (auto)"], ["123", "chevrolet", "chevrolet monte carlo s"], ["316", "pontiac", "pontiac phoenix"], ["216", "plymouth", "plymouth volare premier v8"], ["299", "chevrolet", "chevrolet malibu classic (sw)"], ["394", "datsun", "datsun 310 gx"], ["301", "volkswagen", "vw rabbit custom"], ["335", "audi", "audi 5000s (diesel)"], ["278", "toyota", "toyota celica gt liftback"], ["152", "toyota", "toyota corona"], ["208", "ford", "ford granada ghia"], ["308", "oldsmobile", "oldsmobile cutlass salon brougham"], ["138", "ford", "ford pinto"], ["118", "datsun", "datsun 610"], ["63", "volkswagen", "volkswagen model 111"], ["276", "datsun", "datsun 510"], ["218", "toyota", "toyota mark ii"], ["217", "peugeot", "peugeot 504"], ["161", "chevrolet", "chevrolet nova"]]Table CAR_MAKERS: [["Id", "Maker", "FullName", "Country"], ["22", "kia", "Kia Motors", "8"]]Table CAR_NAMES: [["MakeId", "Model", "Make"], ["191", "opel", "opel 1900"], ["124", "pontiac", "pontiac grand prix"], ["164", "pontiac", "pontiac catalina"], ["398", "ford", "ford granada l"], ["245", "chevrolet", "chevrolet chevette"], ["28", "audi", "audi 100 ls"], ["362", "renault", "renault 18i"], ["226", "renault", "renault 5 gtl"], ["370", "toyota", "toyota cressida"], ["179", "toyota", "toyota corona"], ["312", "fiat", "fiat strada custom"], ["50", "dodge", "dodge monaco (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:Find the name of the makers that produced some cars in the year of 1970? | [["gm"], ["chrysler"], ["amc"], ["ford"], ["citroen"], ["toyota"], ["nissan"], ["volkswagen"], ["peugeaut"], ["saab"], ["bmw"]] | 2,048 | Answer: |
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