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yale-nlp/SciDQA

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Permissive or share-alike

The declared licence (odc-by) is classified as permissive.

Declared licence: odc-by, from the tagEvidence: README.md at 2e4bfd9
Licence files: No licence file at the top level of the repositoryEvidence: file list at 2e4bfd9
Declared sources: None listed in the card metadata
Access gated on Hugging Face: No

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Tasks

question-answering

Citation text from the card

@inproceedings{singh-etal-2024-scidqa,
    title = "{S}ci{DQA}: A Deep Reading Comprehension Dataset over Scientific Papers",
    author = "Singh, Shruti  and
      Sarkar, Nandan  and
      Cohan, Arman",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.1163",
    doi = "10.18653/v1/2024.emnlp-main.1163",
    pages = "20908--20923",
    abstract = "Scientific literature is typically dense, requiring significant background knowledge and deep comprehension for effective engagement. We introduce SciDQA, a new dataset for reading comprehension that challenges language models to deeply understand scientific articles, consisting of 2,937 QA pairs. Unlike other scientific QA datasets, SciDQA sources questions from peer reviews by domain experts and answers by paper authors, ensuring a thorough examination of the literature. We enhance the dataset{'}s quality through a process that carefully decontextualizes the content, tracks the source document across different versions, and incorporates a bibliography for multi-document question-answering. Questions in SciDQA necessitate reasoning across figures, tables, equations, appendices, and supplementary materials, a

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dataset_licence({ "id": "yale-nlp/SciDQA" })
signal: "permissive"
stated_licence: "odc-by"
sources: 0