LawChain: Modeling Legal Reasoning Chains for Chinese Tort Case Analysis

2025-10-20

AI Summary

Existing legal reasoning research predominantly relies on generic frameworks (e.g., syllogism, IRAC), overlooking fine-grained reasoning processes in civil cases—particularly Chinese tort disputes—and lacks domain-specific evaluation benchmarks. To address this, we propose LawChain, the first structured legal reasoning framework tailored to Chinese civil tort cases, comprising three modules: attribution analysis, liability determination, and damage quantification. Concurrently, we introduce LawChain$_{eval}$, a dedicated benchmark for rigorous evaluation. Innovatively, LawChain integrates downstream tasks—including legal named entity recognition and compensation calculation—to assess generalization capability. Experimental results reveal that mainstream large language models exhibit significant weaknesses in critical reasoning steps. Incorporating LawChain yields substantial multi-task performance gains over baselines, establishing a new paradigm for fine-grained modeling and evaluation in civil legal AI.

Technology Category

• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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 and IRAC, which do not comprehensively examine the nuanced processes that underpin 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 for explicitly modeling legal reasoning in the analysis of Chinese tort-related civil cases. We first operationalize the legal reasoning processes used in tort analysis into the LawChain framework. LawChain is a three-module reasoning framework, with each module consisting of multiple finer-grained sub-steps. Informed by the LawChain framework, we introduce the task of tort legal reasoning and construct an evaluation benchmark, LawChain$_{eval}$, to systematically assess the critical steps within analytical reasoning chains for tort analysis. Leveraging this benchmark, we evaluate state-of-the-art 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 LawChain-style reasoning through prompting or post-training. We conduct further experiments on additional legal analysis tasks, such as Legal Named-Entity Recognition and Criminal Damages Calculation, to verify the generalizability of these baselines. The proposed baseline approaches achieve significant improvements in tort-related legal reasoning and generalize well to related legal analysis tasks, thus demonstrating the value of explicitly modeling legal reasoning chains to enhance the reasoning capabilities of language models.

Problem

• Modeling nuanced legal reasoning chains for Chinese tort cases
• Addressing insufficient computational modeling of civil case analysis
• Enhancing language models' reasoning capabilities for legal contexts

Innovation

• LawChain framework models legal reasoning chains
• Three-module structure with fine-grained sub-steps
• Baseline approaches enhance reasoning via prompting and post-training