mlsanityData to 7 Oct 2026

HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

arXiv 1809.09600 ↗First posted 25 Sep 2018cs.CLcited by 3,686

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Cited by 877 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Existing question answering (QA) datasets fail to train QA systems to perform complex reasoning and provide explanations for answers. We introduce HotpotQA, a new dataset with 113k Wikipedia-based question-answer pairs with four key features: (1) the questions require finding and reasoning over multiple supporting documents to answer; (2) the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas; (3) we provide sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain the predictions; (4) we offer a new type of factoid comparison questions to test QA systems' ability to extract relevant facts and perform necessary comparison. We show that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.

Key facts. Cited by 3,686 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Sep 2018.

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Citing papers per year: 2021: 186; 2022: 203; 2023: 253; 2024: 645; 2025: 1062; 2026: 1337.

3,686 papers.

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38 more
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