Hallucination Rates in Legal AI Tools

Two Stanford-affiliated studies shaped the debate. The first, "Large Legal Fictions" (2024), by Matthew Dahl, Varun Magesh, Mirac Suzgun and Daniel Ho, tested general-purpose chatbots on verifiable questions about federal court cases. It found hallucination rates between 69 and 88 percent on those questions. Models also tended to accept false premises built into the questions. Commercial legal vendors argued that their tools avoided this problem through retrieval-augmented generation, which grounds answers in real legal databases. Some marketing used phrases such as "hallucination-free." In 2024, researchers at Stanford's RegLab and Institute for Human-Centered AI tested that claim in a study titled "Hallucination-Free?" They ran more than 200 legal queries through Lexis+ AI, Westlaw AI-Assisted Research and Ask Practical Law AI, with GPT-4 as a comparison. They counted an answer as a hallucination if it was either incorrect or misgrounded. Misgrounded means it cited a real source that does not support the claim. Lexis+ AI performed best, yet still hallucinated on more than 17 percent of queries. Westlaw's tool hallucinated on roughly a third. Ask Practical Law AI often gave incomplete answers. An early version of the paper was criticized because the researchers first lacked access to Westlaw's research tool; they later tested it and revised the paper. Retrieval reduces errors but does not remove them, for several reasons. The retriever may find documents that share words with the query but are not legally relevant. It may miss the controlling authority, or pull a case from the wrong jurisdiction or a superseded version of a rule. The model may then misread or ignore what was retrieved, blend sources together, or go along with a false premise. A common misconception is that a correct-looking citation means a correct answer. The misgrounded category shows that a real citation can support a false claim.