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

By Varun Magesh, Faiz Surani, Matthew Dahl, Mirac Suzgun, Christopher D. Manning, Daniel E. Ho. Journal of Empirical Legal Studies, 2024

341 citations recorded by the source corpus.

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 this article, we design and report on the first preregistered empirical evaluation of AI‐driven legal research tools. We demonstrate that the providers' claims are overstated. While hallucinations are reduced relative to general‐purpose chatbots (GPT‐4), we find that the AI research tools made by LexisNexis (Lexis+ AI) and Thomson Reuters (Westlaw AI‐Assisted Research and Ask Practical Law AI) each hallucinate between 17% and 33% of the time. We also document substantial differences between systems in responsiveness and accuracy. Our article makes four key contributions. It is the first to assess and report the performance of RAG‐based proprietary legal AI tools. Second, it introduces a comprehensive, preregistered dataset for identifying and understanding vulnerabilities in these systems. Third, it proposes a clear typology for differentiating between hallucinations and accurate legal responses. Last, it provides evidence to inform the responsibilities of legal professionals in supervising and verifying AI outputs, which remains a central open question for the responsible integration of AI into law.

Subfields: Artificial Intelligence, Political Science and International Relations, Safety Research

View Paper: https://www.semanticscholar.org/paper/b76e865e070cb353b52a3cb1e50c86ec460da79e

Citations in the corpus

• Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models (2023, neutral): “However, with the advent of LLMs equipped with expansive world knowledge, they can now produce hallucinations that even experts in fields such as medicine (Pal et al., 2023; Umapathi et al., 2023; Chen et al., 2024a) and law (Magesh et al., 2024) find challenging to discern.”
• HistoRAG: Embedding Historical Methodology in Retrieval-Augmented Generation Through Critical Technical Practice (2026, unknown): “In law, Magesh et al. (2025) demonstrated that commercial legal RAG tools hallucinate between 17% and 33% of the time, indicating that domain-focused applications require not just improved retrieval but perhaps different frameworks entirely.”
• LettuceDetect: A Hallucination Detection Framework for RAG Applications (2025, unknown): “As LLMs remain prone to hallucinations, their utilization in high-risk settings, such as medical or legal fields, may be jeopardized (Lozano et al., 2023; Magesh et al., 2024).”
• Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval (2024, unknown): “They tend to retrieve information that, while semantically similar in latent space, fails to provide meaningful clinical insights, leading to suboptimal outcomes for precise healthcare predictions (Shi et al., 2024; Magesh et al., 2024; Li et al., 2024).”