{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/piqa-reasoning-about-physical-commonsense-in","title":"PIQA: Reasoning about Physical Commonsense in Natural Language","arxiv_id":"1911.11641","date":"2019-11-26","proceeding":null,"authors":["Yonatan Bisk","Rowan Zellers","Ronan Le Bras","Jianfeng Gao","Yejin Choi"],"abstract":"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.","url_abs":"https://arxiv.org/abs/1911.11641v1","url_pdf":"https://arxiv.org/pdf/1911.11641v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"piqa-reasoning-about-physical-commonsense-in","repo_url":"https://github.com/AkariAsai/logic_guided_qa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"piqa-reasoning-about-physical-commonsense-in","repo_url":"https://github.com/vered1986/self_talk","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"common-sense-reasoning","task_name":"Common Sense Reasoning"},{"task_slug":"natural-language-understanding","task_name":"Natural Language Understanding"},{"task_slug":"physical-commonsense-reasoning","task_name":"Physical Commonsense Reasoning"},{"task_slug":"question-answering","task_name":"Question Answering"}],"methods":[],"datasets_introduced":[{"slug":"piqa","name":"PIQA","full_name":"Physical Interaction: Question Answering"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/question-answering-on-piqa","task":"Question Answering","dataset":"PIQA","model":"RoBERTa-large 355M (fine-tuned)","rank_in_archive_order":41,"of":67,"metrics":{"Accuracy":"77.1"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-piqa","task":"Question Answering","dataset":"PIQA","model":"GPT-2-small 124M (fine-tuned)","rank_in_archive_order":58,"of":67,"metrics":{"Accuracy":"69.2"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-piqa","task":"Question Answering","dataset":"PIQA","model":"BERT-large 340M (fine-tuned)","rank_in_archive_order":61,"of":67,"metrics":{"Accuracy":"66.8"},"uses_additional_data":false},{"leaderboard":"/sota/question-answering-on-piqa","task":"Question Answering","dataset":"PIQA","model":"Random chance baseline","rank_in_archive_order":67,"of":67,"metrics":{"Accuracy":"50"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1911.11641","atlas_url":"https://app.syntology.ai/?focus=1911.11641","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}