mlsanityData to 7 Oct 2026

AmbigQA: Answering Ambiguous Open-domain Questions

arXiv 2004.10645 ↗First posted 22 Apr 2020cs.CL cs.AIcited by 385

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Cited by 56 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Ambiguity is inherent to open-domain question answering; especially when exploring new topics, it can be difficult to ask questions that have a single, unambiguous answer. In this paper, we introduce AmbigQA, a new open-domain question answering task which involves finding every plausible answer, and then rewriting the question for each one to resolve the ambiguity. To study this task, we construct AmbigNQ, a dataset covering 14,042 questions from NQ-open, an existing open-domain QA benchmark. We find that over half of the questions in NQ-open are ambiguous, with diverse sources of ambiguity such as event and entity references. We also present strong baseline models for AmbigQA which we show benefit from weakly supervised learning that incorporates NQ-open, strongly suggesting our new task and data will support significant future research effort. Our data and baselines are available at https://nlp.cs.washington.edu/ambigqa.

Key facts. Cited by 385 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2020.

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Citing papers per year: 2021: 34; 2022: 29; 2023: 53; 2024: 79; 2025: 97; 2026: 93.

385 papers.

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38 more
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  16. 2023Evaluating Open-Domain Question Answering in the Era of Large Language Modelscited by 110
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  18. 2024The Art of Saying No: Contextual Noncompliance in Language Modelscited by 100
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  21. 2025Uncertainty Quantification and Confidence Calibration in Large Language Models: A Surveycited by 93
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  24. 2021CREAK: A Dataset for Commonsense Reasoning over Entity Knowledgecited by 88
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  32. 2022Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluationcited by 66
  33. 2022CLAM: Selective Clarification for Ambiguous Questions with Generative Language Modelscited by 66
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