LexChain: Modeling Legal Reasoning Chains for Chinese Tort Case Analysis

TL;DR

This paper introduces LexChain, a framework for modeling legal reasoning in Chinese tort civil cases, and evaluates large language models' reasoning abilities, proposing baselines that improve performance by explicitly modeling reasoning chains.

Contribution

The paper presents a novel LexChain framework for explicit legal reasoning modeling in tort cases and creates an evaluation benchmark for assessing reasoning capabilities of language models.

Findings

Current models underperform in tort legal reasoning.

Explicit reasoning modeling improves model performance.

Baseline approaches generalize well to related legal tasks.

Abstract

Legal reasoning is a fundamental component of legal analysis and decision-making. Existing computational approaches to legal reasoning predominantly rely on generic reasoning frameworks such as syllogism, which do not comprehensively examine the nuanced process of legal reasoning. Moreover, current research has largely focused on criminal cases, with insufficient modeling for civil cases. In this work, we present a novel framework to explicitly model legal reasoning in the analysis of Chinese tort-related civil cases. We first operationalize the legal reasoning process in tort analysis into the three-module LexChain framework, with each module consisting of multiple finer-grained sub-steps. Informed by the LexChain framework, we introduce the task of tort legal reasoning and construct an evaluation benchmark to systematically assess the critical steps within analytical reasoning chains for tort analysis. Leveraging this benchmark, we evaluate existing large language models for their legal reasoning ability in civil tort contexts. Our results indicate that current models still fall short in accurately handling crucial elements of tort legal reasoning. Furthermore, we introduce several baseline approaches that explicitly incorporate LexChain-style reasoning through prompting or post-training. The proposed baselines achieve significant improvements in tort-related legal reasoning and generalize well to related legal analysis tasks, demonstrating the value of explicitly modeling legal reasoning chains to enhance the reasoning capabilities of language models.