Legal Rule Induction: Towards Generalizable Principle Discovery from Analogous Judicial Precedents
This work addresses a gap in computational legal research for automating the extraction of implicit legal principles from precedents, though it is incremental as it builds on existing LLM capabilities with new task definitions and datasets.
The paper tackles the problem of inducing generalizable legal rules from analogous judicial precedents, which is understudied due to limitations in model inference and symbolic reasoning, by formalizing Legal Rule Induction (LRI) and introducing a benchmark with 5,121 case sets and 216 expert-annotated test sets, showing that training on this dataset significantly improves LLMs' ability to capture nuanced rule patterns.
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, constrained by limitations in model inference efficacy and symbolic reasoning capability. The advent of Large Language Models (LLMs) offers unprecedented opportunities for automating the extraction of such latent principles, yet progress is stymied by the absence of formal task definitions, benchmark datasets, and methodologies. To address this gap, we formalize Legal Rule Induction (LRI) as the task of deriving concise, generalizable doctrinal rules from sets of analogous precedents, distilling their shared preconditions, normative behaviors, and legal consequences. We introduce the first LRI benchmark, comprising 5,121 case sets (38,088 Chinese cases in total) 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.