mlsanityData to 7 Oct 2026

PIQA: Reasoning about Physical Commonsense in Natural Language

arXiv 1911.11641 ↗First posted 26 Nov 2019cs.CL cs.AI cs.LGcited by 2,818

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Cited by 226 in Mixture-of-experts, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

To apply eyeshadow without a brush, should I use a cotton swab or a toothpick? Questions requiring this kind of physical commonsense pose a challenge to today's natural language understanding systems. While recent pretrained models (such as BERT) have made progress on question answering over more abstract domains - such as news articles and encyclopedia entries, where text is plentiful - in more physical domains, text is inherently limited due to reporting bias. Can AI systems learn to reliably answer physical common-sense questions without experiencing the physical world? In this paper, we introduce the task of physical commonsense reasoning and a corresponding benchmark dataset Physical Interaction: Question Answering or PIQA. Though humans find the dataset easy (95% accuracy), large pretrained models struggle (77%). We provide analysis about the dimensions of knowledge that existing models lack, which offers significant opportunities for future research.

Key facts. Cited by 2,818 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Nov 2019.

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Citing papers per year: 2020: 1; 2021: 51; 2022: 101; 2023: 243; 2024: 709; 2025: 897; 2026: 816.

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