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

arXiv: https://arxiv.org/abs/2610.19427 · HTML: https://mlsanity.com/paper/2610.19427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 14 Oct 2026.

## Placed in

- LLM reasoning

## Builds on (most cited first, 10 of 10)

- [CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction](https://mlsanity.com/paper/1807.02478/) (2018)
- [Blind Judgement: Agent-Based Supreme Court Modelling With GPT](https://mlsanity.com/paper/2301.05327/) (2023)
- [Legal Syllogism Prompting: Teaching Large Language Models for Legal Judgment Prediction](https://mlsanity.com/paper/2307.08321/) (2023)
- [A Comprehensive Evaluation of Large Language Models on Legal Judgment Prediction](https://mlsanity.com/paper/2310.11761/) (2023)
- [Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction](https://mlsanity.com/paper/2407.01964/) (2024)
- [Legal Fact Prediction: The Missing Piece in Legal Judgment Prediction](https://mlsanity.com/paper/2409.07055/) (2024)
- [Predicting the Behavior of the Supreme Court of the United States: A General Approach](https://mlsanity.com/paper/1407.6333/) (2014)
- [JudgeMeNot: Personalizing Large Language Models to Emulate Judicial Reasoning in Hebrew](https://mlsanity.com/paper/2604.18041/) (2026)
- [The Cases LJP Never Sees: Prosecution Decision Prediction for More Complete Criminal Liability Assessment](https://mlsanity.com/paper/2605.28464/) (2026)
- [Towards Explainable Adjudicative Variance: Quantifying Judicial Discretion via Gated Multi-Task Learning](https://mlsanity.com/paper/2606.27069/) (2026)

## 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.