@inproceedings{fan-etal-2026-legal,
title = "Legal Rule Induction: Towards Generalizable Principle Discovery from Analogous Judicial Precedents",
author = "Fan, Wei and
Zheng, Tianshi and
HU, Yiran and
Deng, Zheye and
Wang, Weiqi and
Xu, Baixuan and
Li, Chunyang and
Li, Haoran and
Shen, Weixing and
Song, Yangqiu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.963/",
pages = "22212--22240",
ISBN = "979-8-89176-459-0",
abstract = "Legal rules encompass not only codified statutes but also implicit adjudicatory principles derived from precedents that contain discretionary norms, social morality, and policy. While computational legal research has advanced in applying established rules to cases, inducing legal rules from judicial decisions remains understudied across jurisdictions. The advent of Large Language Models (LLMs) offers unprecedented potential to automate the extraction of such latent principles, yet progress is stymied by the absence of formal task definitions and benchmarks. To address this gap, we formalize Legal Rule Induction (LRI) as the task of deriving concise, generalizable doctrinal rules from analogous precedents, distilling their shared preconditions, normative behaviors, and legal consequences. We further propose a reproducible pipeline for LRI dataset construction and, instantiating it on Chinese law as a representative jurisdiction, introduce the first LRI benchmark comprising 5,121 case sets (38,088 court cases) for model tuning and 216 expert-annotated gold test sets. Experimental results reveal that: 1) State-of-the-art LLMs struggle with over-generalization and hallucination; 2) Training on our dataset markedly enhances LLMs' capabilities in capturing nuanced rule patterns across similar cases."
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<abstract>Legal rules encompass not only codified statutes but also implicit adjudicatory principles derived from precedents that contain discretionary norms, social morality, and policy. While computational legal research has advanced in applying established rules to cases, inducing legal rules from judicial decisions remains understudied across jurisdictions. The advent of Large Language Models (LLMs) offers unprecedented potential to automate the extraction of such latent principles, yet progress is stymied by the absence of formal task definitions and benchmarks. To address this gap, we formalize Legal Rule Induction (LRI) as the task of deriving concise, generalizable doctrinal rules from analogous precedents, distilling their shared preconditions, normative behaviors, and legal consequences. We further propose a reproducible pipeline for LRI dataset construction and, instantiating it on Chinese law as a representative jurisdiction, introduce the first LRI benchmark comprising 5,121 case sets (38,088 court cases) for model tuning and 216 expert-annotated gold test sets. Experimental results reveal that: 1) State-of-the-art LLMs struggle with over-generalization and hallucination; 2) Training on our dataset markedly enhances LLMs’ capabilities in capturing nuanced rule patterns across similar cases.</abstract>
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%0 Conference Proceedings
%T Legal Rule Induction: Towards Generalizable Principle Discovery from Analogous Judicial Precedents
%A Fan, Wei
%A Zheng, Tianshi
%A HU, Yiran
%A Deng, Zheye
%A Wang, Weiqi
%A Xu, Baixuan
%A Li, Chunyang
%A Li, Haoran
%A Shen, Weixing
%A Song, Yangqiu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F fan-etal-2026-legal
%X Legal rules encompass not only codified statutes but also implicit adjudicatory principles derived from precedents that contain discretionary norms, social morality, and policy. While computational legal research has advanced in applying established rules to cases, inducing legal rules from judicial decisions remains understudied across jurisdictions. The advent of Large Language Models (LLMs) offers unprecedented potential to automate the extraction of such latent principles, yet progress is stymied by the absence of formal task definitions and benchmarks. To address this gap, we formalize Legal Rule Induction (LRI) as the task of deriving concise, generalizable doctrinal rules from analogous precedents, distilling their shared preconditions, normative behaviors, and legal consequences. We further propose a reproducible pipeline for LRI dataset construction and, instantiating it on Chinese law as a representative jurisdiction, introduce the first LRI benchmark comprising 5,121 case sets (38,088 court cases) for model tuning and 216 expert-annotated gold test sets. Experimental results reveal that: 1) State-of-the-art LLMs struggle with over-generalization and hallucination; 2) Training on our dataset markedly enhances LLMs’ capabilities in capturing nuanced rule patterns across similar cases.
%U https://aclanthology.org/2026.emnlp-main.963/
%P 22212-22240
Markdown (Informal)
[Legal Rule Induction: Towards Generalizable Principle Discovery from Analogous Judicial Precedents](https://aclanthology.org/2026.emnlp-main.963/) (Fan et al., EMNLP 2026)
ACL
- Wei Fan, Tianshi Zheng, Yiran HU, Zheye Deng, Weiqi Wang, Baixuan Xu, Chunyang Li, Haoran Li, Weixing Shen, and Yangqiu Song. 2026. Legal Rule Induction: Towards Generalizable Principle Discovery from Analogous Judicial Precedents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 22212–22240, Budapest, Hungary. Association for Computational Linguistics.