The paper argues that hallucinations by legal language models should be judged as failures of legal warrant rather than mere factual or citation errors. It defines claim-authority warrant as a context-sensitive relationship between a legal claim and applicable, current authority, and proposes that evaluating warrant can uncover failures missed by traditional accuracy or citation metrics. The authors outline a pilot study, benchmark specifications, and a research agenda to assess whether legal AI systems’ claims are properly licensed by law.
arXiv:2608.21409v1 Announce Type: cross Abstract: In medicine, claims remain valid when supported by empirical evidence grounded in stable biological reality. In law, by contrast, truth is contingent...
arXiv:2606. 15646v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed natural language processing, but their lack of interpretable reasoning and tendency to hallucinate pose significant challenges for legal applications.
The paper introduces PARCEL, a benchmark designed to evaluate whether legal claims are supported by cited authorities. Using 3,396 claims from New York State Court of Appeals decisions, the authors label each claim as Supported, Refuted, or Not Found and cast the task as a three‑way natural language inference problem. State‑of‑the‑art LLMs achieve up to 0.97 accuracy in zero‑shot settings, yet they still frequently misclassify unsupported claims as supported, especially when fabricated but plausible citations are present.
Legal Research Bench (LRB) is a new benchmark comprising 413 open-ended U.S. legal research questions, each paired with a gold answer, supporting authorities, and a binary grading rubric. The study evaluates thirteen advanced language‑model agents using web search, case‑law search, page parsing, and retrieval tools, scoring responses only when all required criteria are met and cited authorities verify. Results show that even the best model, Claude Opus 4.8, achieves full correctness on only 42.9% of questions, with performance varying by legal area and task complexity, and no clear link between more tool calls or inference cost and higher accuracy.
The paper introduces a systematic method for comparing different formalizations of the same legal provision by analyzing their inferences on individual cases. It matches formalizations at the node level, derives shared interfaces, and uses a SAT solver to identify edge cases where any two formalizations disagree. The authors apply this approach to ten EU provisions formalized by nine advanced LLMs, finding that behavioral divergence is largely uncorrelated with structural agreement and that the resulting edge cases expose distinct types of disagreement, some reflecting real legal controversies.
arXiv:2610.19427v1 Announce Type: new Abstract: Legal reasoning is a fundamental capability for large language models (LLMs) to complete legal AI tasks such as legal judgment prediction. In legal pra...
arXiv:2606. 18021v1 Announce Type: new Abstract: AI systems deployed in legal workflows hallucinate at rates that aggregate metrics report at ~52%, but this average conceals where errors concentrate and in which direction they run, leaving compliance officers without an actionable signal for trustworthy deployment.
arXiv:2608. 14610v1 Announce Type: new Abstract: Legal reasoning tasks such as legal judgment prediction (LJP) require identifying the temporally correct version of the law governing a case -- a capability we term temporal applicable-law determination.
arXiv:2606. 23716v1 Announce Type: cross Abstract: Legal AI benchmark research frequently invokes the assumption that large language models can improve access to justice, including for people who cannot access lawyers in order to understand and exercise their legal rights.
The study examines whether large language models truly base legal verdicts on the statutes or precedents they cite. By fixing case facts and swapping the cited authority with an unrelated one, researchers found that while models correctly name the authority 66.7%-100% of the time, the resulting verdict often remains unchanged—only 0.0%-21.7% of the time on CaseHOLD, 30.0%-76.7% on ECHR and SCOTUS, and 43.3%-50.0% on ContractNLI. Even a purpose‑built legal‑reasoning model and larger scales did not close this gap, and adversarial instructions further exposed vulnerabilities in verdict consistency.
arXiv:2609.23726v1 Announce Type: new Abstract: Large language models have shown strong performance across a range of legal tasks, but existing benchmarks rarely evaluate the ability to take and defe...
Large language models often justify legal decisions by citing statutes or precedents, but this study shows that while models correctly name the governing authority 66.7%-100% of the time, the actual verdict rarely changes when the cited authority is swapped—only 0.0%-21.7% on CaseHOLD, 30.0%-76.7% on ECHR and SCOTUS, and 43.3%-50.0% on ContractNLI. This gap persists across models of different sizes and even a purpose‑built legal‑reasoning model. Additionally, a red‑team evaluation revealed that models are more compliant with hidden adversarial instructions than they are sensitive to verdict changes, indicating that citing legal authority is a weak proxy for genuine reasoning and that verdicts remain vulnerable to manipulation.