用法律事实与推理构建案件嵌入，提升判例检索准确率。
ReaKase-8B: Legal Case Retrieval via Knowledge and Reasoning Representations with LLMs
- 通过提取法律事实、问题、关系三元组和推理链构建案件表示
- 在COLIEE 2022/2023上优于基线模型，显著提升检索效果
- 适合法律AI研究者与智能司法系统开发者参考
法律案例检索（LCR）是实际法律决策的核心，帮助从业者为给定案例寻找判例依据。现有方法主要依赖传统词法模型和预训练语言模型对案例文本编码，但忽略了法律实体间的关系及揭示法律事实如何导致判决的推理过程。这些关系与推理过程体现了案例的独特性，反映了真实的司法逻辑。本文提出ReaKase-8B框架，利用提取的法律事实、法律问题、法律关系三元组和推理信息，构建增强型案件嵌入。该框架采用微调的大语言模型设计上下文案例表征学习范式。在COLIEE 2022和COLIEE 2023两个基准数据集上的实验表明，融合知识与推理的嵌入显著优于基线模型，证明了将法律推理融入案例检索系统的潜力。代码已开源。
原文摘要 · Abstract (English)
Legal case retrieval (LCR) is a cornerstone of real-world legal decision making, as it enables practitioners to identify precedents for a given query case. Existing approaches mainly rely on traditional lexical models and pretrained language models to encode the texts of legal cases. Yet there are rich information in the relations among different legal entities as well as the crucial reasoning process that uncovers how legal facts and legal issues can lead to judicial decisions. Such relational reasoning process reflects the distinctive characteristics of each case that can distinguish one from another, mirroring the real-world judicial process. Naturally, incorporating such information into the precise case embedding could further enhance the accuracy of case retrieval. In this paper, a novel ReaKase-8B framework is proposed to leverage extracted legal facts, legal issues, legal relation triplets and legal reasoning for effective legal case retrieval. ReaKase-8B designs an in-context legal case representation learning paradigm with a fine-tuned large language model. Extensive experiments on two benchmark datasets from COLIEE 2022 and COLIEE 2023 demonstrate that our knowledge and reasoning augmented embeddings substantially improve retrieval performance over baseline models, highlighting the potential of integrating legal reasoning into legal case retrieval systems. The code has been released on https://github.com/yanran-tang/ReaKase-8B.
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