Datasets:
Tasks:
Question Answering
Modalities:
Text
Formats:
json
Languages:
English
Size:
10K - 100K
License:
Update README.md
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README.md
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---
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license: cc-by-4.0
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---
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license: cc-by-4.0
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task_categories:
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- question-answering
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language:
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- en
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size_categories:
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- 10K<n<100K
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---
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## Literature Synthesis Queries
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This dataset contains 50k real-world literature synthesis queries from [our public demo](https://openscilm.allen.ai/).
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### Dataset Summary
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This dataset contains real-world literature synthesis questions collected from users of a scientific question-answering system.
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Each entry includes:
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- The user’s query (in natural language) (`query`)
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- The subject of the question (e.g., computer science, medicine, engineering) (`subject`)
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- The query intent (e.g., Literature Understanding, Paper Finding, Ideation, Other) (`query intent`)
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The questions reflect realistic information needs from researchers and students, and often require retrieving, synthesizing, or summarizing scientific literature.
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The dataset is intended for research on retrieval-augmented generation, scientific question answering, and user intent classification.
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### License
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This dataset is licensed under [ODC-BY](https://creativecommons.org/licenses/by/4.0/legalcode.en). It is intended for research and educational use in accordance with [Ai2's Responsible Use Guidelines](https://allenai.org/responsible-use). This dataset includes output data generated from third-party models that are subject to separate terms governing their use.
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### Citation
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```
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@article{openscholar,
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title={{OpenScholar}: Synthesizing Scientific Literature with Retrieval-Augmented Language Models},
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author={Asai, Akari and He*, Jacqueline and Shao*, Rulin and Shi, Weijia and Singh, Amanpreet and Chang, Joseph Chee and Lo, Kyle and Soldaini, Luca and Feldman, Tian, Sergey and Mike, D’arcy and Wadden, David and Latzke, Matt and Sparks,Jenna and Hwang, Jena D. and Kishore, Varsha and Minyang and Ji, Pan and Liu, Shengyan and Tong, Hao and Wu, Bohao and Xiong, Yanyu and Zettlemoyer, Luke and Weld, Dan and Neubig, Graham and Downey, Doug and Yih, Wen-tau and Koh, Pang Wei and Hajishirzi, Hannaneh},
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journal={Arxiv},
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year={2024},
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}
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```
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