构建中文民事诉讼争议点识别基准，提升法律AI对核心争议的判断能力。
LexIssue: Benchmarking Legal Issue Identification in Chinese Civil Litigation
- 提出分层法律争议表示框架，结合自由描述与结构化分类。
- 建立含430个真实案例、1303个专家标注争议点的LexIssue基准。
- 引入法律知识库增强生成，显著提升争议点识别准确率。
识别诉讼双方争议焦点是实际诉讼中的关键环节，但法律争议在法律AI研究中仍相对未受重视。本文研究诉讼中法律争议识别的计算建模问题，提出一种基于法律事实的分层表示框架，通过自由形式的争议描述和结构化法律类别共同刻画法律争议，并将争议识别拆解为法律争议生成与分类两个互补任务。基于此，构建了LexIssue基准，包含430个真实中国民事诉讼案例及1,303个专家标注的争议点。进一步构建了一个以27种诉由和441个候选法律争议条目为基础的以争议为中心的法律知识库，支持检索增强推理。在多种模型上的实验表明，使用该知识库进行检索增强生成，能持续提升争议点及其对应法律属性的识别性能。
原文摘要 · Abstract (English)
Identifying the issues disputed between litigating parties is a crucial component of real-world litigation. However, legal issues remain comparatively underexplored in legal AI research. In this work, we study the computational modelling of legal issue identification in litigation. We introduce a legally grounded hierarchical schema that represents legal issues through both free-form issue descriptions and structured legal categories, and formulate legal issue identification as two complementary tasks: legal issue generation and legal issue classification. Based on this formulation, we construct LexIssue, a benchmark containing 430 real-world Chinese civil litigation cases and 1,303 expert-annotated disputed legal issues. We further develop an issue-centric legal knowledge base spanning 27 causes of action and 441 candidate legal issue entries to support retrieval-augmented reasoning. Experimental results across a diverse set of models show that retrieval-augmented generation using the constructed legal issue knowledge base consistently improves performance in identifying disputed legal issues and their corresponding legal attributes.
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