Legal case retrieval and judgment prediction are crucial components in intelligent legal systems. In practice, determining whether two cases share the same charges through legal judgment prediction is essential for establishing their relevance in case retrieval. However, current studies on legal case retrieval merely focus on the semantic similarity between paired cases, ignoring their charge-level consistency. This separation leads to a lack of context and potential inaccuracies in the case retrieval that can undermine trust in the system's decision-making process. Given the guidance role of laws to both tasks and inspired by the success of generative retrieval, in this work, we propose to incorporate judgment prediction into legal case retrieval, achieving a novel law-aware Generative legal case retrieval method called Gear. Specifically, Gear first extracts rationales (key circumstances and key elements) for legal cases according to the definition of charges in laws, ensuring a shared and informative representation for both tasks. Then in accordance with the inherent hierarchy of laws, we construct a law structure constraint tree and assign law-aware semantic identifier(s) to each case based on this tree. These designs enable a unified traversal from the root, through intermediate charge nodes, to case-specific leaf nodes, which respectively correspond to two tasks. Additionally, in the training, we also introduce a revision loss that jointly minimizes the discrepancy between the identifiers of predicted and labeled charges as well as retrieved cases, improving the accuracy and consistency for both tasks. Extensive experiments on two datasets demonstrate that Gear consistently outperforms state-of-the-art methods in legal case retrieval while maintaining competitive judgment prediction performance.

翻译：法律案例检索与判决预测是智能法律系统中的关键组成部分。在实践中，通过法律判决预测确定两个案件是否具有相同案由，对于建立案件检索中的相关性至关重要。然而，当前法律案例检索研究仅关注案件对之间的语义相似性，忽略了案由层面的一致性。这种割裂导致案件检索缺乏上下文并产生潜在偏差，可能损害对系统决策过程的信任。鉴于法律对两项任务的指导作用，并受生成式检索成功经验的启发，本文提出将判决预测融入法律案例检索，实现了一种新颖的法律感知生成式法律案例检索方法——Gear。具体而言，Gear首先根据法律条文对案由的定义，提取法律案例的理据（关键情节与关键要素），确保两项任务共享信息丰富的表征。随后，依据法律固有的层级结构，构建法律结构约束树，并基于该树为每个案例分配法律感知语义标识符。这些设计实现了从根节点、经中间案由节点至案例特定叶节点的统一遍历，分别对应两项任务。此外，在训练过程中引入修正损失函数，通过联合最小化预测案由与标注案由标识符之间、以及检索案例与目标案例标识符之间的差异，提升两项任务的准确性与一致性。在两个数据集上的大量实验表明，Gear在法律案例检索中持续优于现有最优方法，同时保持具有竞争力的判决预测性能。