Hallucination‐Free? Assessing the Reliability of Leading AI Legal Research Tools

ABSTRACT Legal practice has witnessed a sharp rise in products incorporating artificial intelligence (AI). Such tools are designed to assist with a wide range of core legal tasks, from search and summarization of caselaw to document drafting. However, the large language models used in these tools are prone to “hallucinate,” or make up false information, making their use risky in high‐stakes domains. Recently, certain legal research providers have touted methods such as retrieval‐augmented generation (RAG) as “eliminating” or “avoid[ing]” hallucinations, or guaranteeing “hallucination‐free” legal citations. Because of the closed nature of these systems, systematically assessing these claims is challenging. In…

• FU
  Fordham University
• UO
  University of Toronto
• SL
  Stanford Law School