mlsanityData to 14 Oct 2026

How Do LLMs Understand Legal Philosophy? Diagnosing Legal-Philosophical Reasoning in LLMs

arXiv 2610.19427 ↗First posted 14 Oct 2026cs.CL cs.AIcited by 0

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Abstract (arXiv)

Legal reasoning is a fundamental capability for large language models (LLMs) to complete legal AI tasks such as legal judgment prediction. In legal practice, this reasoning is shaped by legal philosophy: a judge's view of the nature and sources of law guides how legal materials are interpreted and applied, so judges working from the same statutes and precedents can reach different judgments. Existing research has largely overlooked whether LLMs can understand legal philosophy and apply it in legal reasoning, an ability we call textitlegal-philosophical reasoning. To fill this gap, we introduce LePhilBench, a benchmark for evaluating the legal-philosophical reasoning of LLMs that comprises two benchmarking tasks and a dataset. The two tasks evaluate how well LLMs understand legal philosophy and how well they apply it in legal reasoning, respectively. The dataset is manually and meticulously constructed. Its test set contains 220 U.S. Supreme Court opinions. A law professor designed fourteen annotation questions on legal philosophy, and three law-trained annotators with formal training in jurisprudence answered all fourteen questions for every opinion, yielding 9,240 legal-philosophical labels. LePhilBench takes the first step toward exploring legal-philosophical reasoning in LLMs.

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References · 10

  1. 2018CAIL2018: A Large-Scale Legal Dataset for Judgment Predictioncited by 170
  2. 2023Blind Judgement: Agent-Based Supreme Court Modelling With GPTcited by 38
  3. 2023Legal Syllogism Prompting: Teaching Large Language Models for Legal Judgment Predictioncited by 38
  4. 2023A Comprehensive Evaluation of Large Language Models on Legal Judgment Predictioncited by 28
  5. 2024Enabling Discriminative Reasoning in LLMs for Legal Judgment Predictioncited by 17
  6. 2024Legal Fact Prediction: The Missing Piece in Legal Judgment Predictioncited by 5
  7. 2014Predicting the Behavior of the Supreme Court of the United States: A General Approachcited by 4
  8. 2026JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrewcited by 1
  9. 2026The Cases LJP Never Sees: Prosecution Decision Prediction for More Complete Criminal Liability Assessmentcited by 1
  10. 2026Towards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task Learningcited by 1

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  1. 2023LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models
  2. 2026Can LLMs Reason in a Legally Meaningful Manner? A Small-scale Study on European Court of Human Rights Cases
  3. 2023LawBench: Benchmarking Legal Knowledge of Large Language Models
  4. 2025Thinking Longer, Not Always Smarter: Evaluating LLM Capabilities in Hierarchical Legal Reasoning
  5. 2026Know Your Limits : On the Faithfulness of LLMs as Solvers and Autoformalizers in Legal Reasoning
  6. 2025LeMAJ (Legal LLM-as-a-Judge): Bridging Legal Reasoning and LLM Evaluation
  7. 2026When Do LLMs Apply the Wrong Law? Diagnosing LLM Failures in Temporal Legal Reasoning
  8. 2025Investigating the Shortcomings of LLMs in Step-by-Step Legal Reasoning