Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools
Journal of Empirical Legal Studies 22(2), 216–242
What it adds
A preregistered, manually labelled evaluation of proprietary legal RAG products.
Read with this limitation
Small, curated sample; changing proprietary systems prevent conclusions about current versions or other workflows.
- Jurisdiction tags
- United States
- Language
- English
Follow the source
Selected statements located in the linked primary sources. These results have not been independently reproduced here.
The evaluation uses 202 queries across four legal-query families.
5.2; 7, printed 222–224 and 231The historical tested products hallucinated in 17–33% of responses under the study rubric.
Abstract; 6.1 and Figure 4, printed 216 and 224–225
Connected research
Cites
LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
Uses 20 Rule QA questions.
Inspect the sourceRelated question
Benchmarking Legal RAG: The Promise and Limits of AI Statutory Surveys
Both evaluate legal research tools against source evidence. Benchmarking Legal RAG concentrates on statutory surveys and the reliability of benchmark answers. This is a thematic connection.
Entry status, source edition & rights
- Index entry
- Source-linked summary, prepared with agent assistance. Independent scholarly review is pending.
- Archive admission
- This bibliographic entry does not confer admission to the versioned archive.
- Edition inspected
- 10.1111/jels.12413
- Reading coverage
- Journal sections 4–7, Figure 4, data availability and cited references; earlier arXiv and Stanford drafts compared selectively. Appendices not exhaustively audited.
- Rights
- External references only. Papers and research artifacts remain with their original hosts; their own terms apply.
- Entry updated
- 2026-10-15
Source-linked summary · independent assessment pending.