Legal document retrieval and judgment prediction are crucial tasks in intelligent legal systems. In practice, determining whether two documents share the same judgments is essential for establishing their relevance in legal retrieval. However, existing legal retrieval studies either ignore the vital role of judgment prediction or rely on implicit training objectives, expecting a proper alignment of legal documents in vector space based on their judgments. Neither approach provides explicit evidence of judgment consistency for relevance modeling, leading to inaccuracies and a lack of transparency in retrieval. To address this issue, we propose a law-guided method, namely GEAR, within the generative retrieval framework. GEAR explicitly integrates judgment prediction with legal document retrieval in a sequence-to-sequence manner. Experiments on two Chinese legal case retrieval datasets show the superiority of GEAR over state-of-the-art methods while maintaining competitive judgment prediction performance. Moreover, we validate its robustness across languages and domains on a French statutory article retrieval dataset.

翻译：法律文档检索与判决预测是智能法律系统中的关键任务。在实践中，判定两个文档是否具有相同判决结果对于确立它们在法律检索中的相关性至关重要。然而，现有法律检索研究要么忽略了判决预测的重要作用，要么依赖隐式训练目标，期望基于判决结果在向量空间中实现法律文档的合理对齐。这两种方法均未为相关性建模提供判决一致性的显式证据，导致检索不准确且缺乏可解释性。为解决此问题，我们提出了一种法导方法——GEAR，该方法基于生成式检索框架。GEAR以序列到序列的方式将判决预测与法律文档检索显式整合。在两个中文法律案例检索数据集上的实验表明，GEAR在保持竞争力的判决预测性能的同时，优于现有最优方法。此外，我们在一个法语法条检索数据集上验证了其跨语言和跨领域的鲁棒性。