LexIssue：面向中国民事诉讼的法律问题识别基准测试

中文摘要 AI 辅助

识别诉讼当事人之间的争议问题是现实世界诉讼的关键组成部分，但法律问题在法律AI研究中仍相对未被充分探索。本研究针对诉讼中的法律问题识别开展计算建模，提出一种基于法律依据的分层架构，通过自由形式的问题描述和结构化法律类别来表征法律问题，并将法律问题识别构建为两个互补任务：法律问题生成与法律问题分类。基于该框架，构建了LexIssue基准，包含430个真实世界中国民事诉讼案例及1303个专家标注的争议法律问题；还开发了以问题为中心的法律知识库，涵盖27种案由和441个候选法律问题条目，以支持检索增强推理。对多种不同模型的实验结果表明，利用所构建的法律问题知识库进行检索增强生成，可持续提升争议法律问题及其对应法律属性的识别性能。

英文摘要

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.

发表机构

• Tsinghua University（清华大学）
• Peking University（北京大学）
• Modelbest Inc.（模贝斯特公司）
• Northeastern University（东北大学）

机构由 AI 辅助整理，请以论文原文为准。