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.

翻译：法律推理是法律分析与决策的基础组成部分。现有的法律推理计算方法主要依赖于三段论等通用推理框架，未能全面考察法律推理的细致过程。此外，当前研究大多集中于刑事案件，对民事案件的建模不足。本研究提出了一种新颖的框架，用于在中国侵权相关民事案件分析中显式建模法律推理。我们首先将侵权分析中的法律推理过程操作化为包含三个模块的LexChain框架，每个模块由多个更细粒度的子步骤构成。基于LexChain框架，我们引入了侵权法律推理任务，并构建了一个评估基准，以系统评估侵权分析推理链中的关键步骤。利用该基准，我们评估了现有大型语言模型在民事侵权情境下的法律推理能力。结果表明，当前模型在准确处理侵权法律推理的关键要素方面仍存在不足。此外，我们提出了几种基线方法，通过提示或后训练显式地融入LexChain式推理。所提出的基线方法在侵权相关法律推理上取得了显著提升，并能良好地泛化至相关法律分析任务，证明了显式建模法律推理链对于增强语言模型推理能力的价值。