{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T01:47:55Z","timestamp":1790041675282,"version":"4.0.1"},"reference-count":121,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"content-version":"vor","delay-in-days":177,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Do large language models (LLMs) know the law? LLMs are increasingly being used to augment legal practice, education, and research, yet their revolutionary potential is threatened by the presence of \u201challucinations\u201d\u2014textual output that is not consistent with legal facts. We present the first systematic evidence of these hallucinations in public-facing LLMs, documenting trends across jurisdictions, courts, time periods, and cases. Using OpenAI\u2019s ChatGPT 4 and other public models, we show that LLMs hallucinate at least 58% of the time, struggle to predict their own hallucinations, and often uncritically accept users\u2019 incorrect legal assumptions. We conclude by cautioning against the rapid and unsupervised integration of popular LLMs into legal tasks, and we develop a typology of legal hallucinations to guide future research in this area.<\/jats:p>","DOI":"10.1093\/jla\/laae003","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T15:47:56Z","timestamp":1714146476000},"page":"64-93","source":"Crossref","is-referenced-by-count":283,"title":["Large Legal Fictions: Profiling Legal Hallucinations in Large Language Models"],"prefix":"10.1093","volume":"16","author":[{"given":"Matthew","family":"Dahl","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Varun","family":"Magesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mirac","family":"Suzgun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel E","family":"Ho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,6,26]]},"reference":[{"key":"2024062610572072500_CIT0001","article-title":"Do Language Models Know When They\u2019re Hallucinating References","volume-title":"Findings of the Association for Computational Linguistics: EACL 2024, 912-928","author":"Agrawal","year":"2023"},{"key":"2024062610572072500_CIT0002","article-title":"As Allen & Overy Deploys GPT-based Legal App Harvey Firmwide, Founders Say Other Firms Will Soon Follow","author":"Ambrogi","year":"2023","journal-title":"LawSites"},{"key":"2024062610572072500_CIT0003","article-title":"PaLM 2 Technical Report","author":"Anil","year":"2023"},{"key":"2024062610572072500_CIT0004","first-page":"136","article-title":"Translating Legalese: Enhancing Public Understanding of Court Opinions with Legal Summarizers","author":"Ash","year":"2024"},{"key":"2024062610572072500_CIT0005","doi-asserted-by":"crossref","DOI":"10.18653\/v1\/2023.findings-emnlp.68","article-title":"The Internal State of an LLM Knows When It\u2019s Lying","author":"Azaria","year":"2023"},{"key":"2024062610572072500_CIT0006","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/jla\/laad003","article-title":"Algorithmic Harm in Consumer Markets","volume":"15","author":"Bar-Gill","year":"2023","journal-title":"J. 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