Abstract
Legal relations form a highly consequential analytical framework of civil law system, serving as a crucial foundation for resolving disputes and realizing values of the rule of law in judicial practice. However, legal relations in Chinese civil cases remain underexplored in the field of legal artificial intelligence (legal AI), largely due to the absence of comprehensive schemas. In this work, we firstly introduce a comprehensive schema, which contains a hierarchical taxonomy and definitions of arguments, for AI systems to capture legal relations in Chinese civil cases. Based on this schema, we then formulate legal relation extraction task and present LexRel, an expert-annotated benchmark for legal relation extraction in Chinese civil law. We use LexRel to evaluate state-of-the-art large language models (LLMs) on legal relation extractions, showing that current LLMs exhibit significant limitations in accurately identifying civil legal relations. Furthermore, we demonstrate that incorporating legal relations information leads to consistent performance gains on other downstream legal AI tasks.
Abstract (translated)
法律关系构成了民法体系中一个高度重要的分析框架，是解决纠纷和实现法治价值的重要基础。然而，在中国民事案件中，由于缺乏全面的架构，法律关系在法律人工智能（legal AI）领域仍被较少研究。在这项工作中，我们首先介绍了一个全面的架构，包括法律关系的分层分类法及其定义，使AI系统能够捕捉到中国民事案件中的法律关系。基于这一架构，我们将法律关系抽取任务具体化，并提出了LexRel，一个专家标注的数据集，作为中文民法领域中法律关系提取的基准。我们使用LexRel来评估当前最先进的大型语言模型（LLMs）在法律关系抽取上的表现，显示出现有LLMs在准确识别民事法律关系方面存在显著局限性。此外，我们还证明了将法律关系信息融入其中可以持续提升其他下游法律人工智能任务的表现。
URL
https://arxiv.org/abs/2512.12643
