用大模型与图神经网络提升法律案例检索精度
UQLegalAI@COLIEE2025: Advancing Legal Case Retrieval with Large Language Models and Graph Neural Networks
- 构建法律案例图，用大模型生成文本嵌入作为节点特征
- 引入基于节点度的对比学习目标，优化案例关联信息利用
- 适合法律AI研究者和司法科技开发者参考
法律案例检索在法律领域至关重要，有助于高效识别相关案例，支持法律从业者和研究人员提出法律论点并做出明智决策。为提升检索准确率，每年举办法律信息抽取与蕴含竞赛（COLIEE），提供更新的基准数据集用于评估。本文详细描述了UQLegalAI团队在COLIEE 2025任务1中排名第二所采用的方法——CaseLink。该模型结合归纳式图学习与全局案例图，捕捉案例间的内在关联以提升检索精度。具体而言，使用专用于文本嵌入的大语言模型将法律文本转换为嵌入表示，作为构建案例图中节点的特征。提出一种新的对比学习目标，对案例节点度施加正则化，以更好利用案例引用关系进行模型优化。方法的主要代码库基于开源项目CaseLink：https://github.com/yanran-tang/CaseLink。
原文摘要 · Abstract (English)
Legal case retrieval plays a pivotal role in the legal domain by facilitating the efficient identification of relevant cases, supporting legal professionals and researchers to propose legal arguments and make informed decision-making. To improve retrieval accuracy, the Competition on Legal Information Extraction and Entailment (COLIEE) is held annually, offering updated benchmark datasets for evaluation. This paper presents a detailed description of CaseLink, the method employed by UQLegalAI, the second highest team in Task 1 of COLIEE 2025. The CaseLink model utilises inductive graph learning and Global Case Graphs to capture the intrinsic case connectivity to improve the accuracy of legal case retrieval. Specifically, a large language model specialized in text embedding is employed to transform legal texts into embeddings, which serve as the feature representations of the nodes in the constructed case graph. A new contrastive objective, incorporating a regularization on the degree of case nodes, is proposed to leverage the information within the case reference relationship for model optimization. The main codebase used in our method is based on an open-sourced repo of CaseLink: https://github.com/yanran-tang/CaseLink.
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