将判决预测与法律文档检索显式整合：一种法律引导的生成式方法
Explicitly Integrating Judgment Prediction with Legal Document Retrieval: A Law-Guided Generative Approach
AI总结：
针对现有法律检索未显式结合判决预测导致准确率低、透明度不足的问题，提出法律引导的生成式检索方法GEAR，以序列到序列方式显式整合两类任务，在多个数据集上取得更优效果与鲁棒性。
摘要：
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，判定两份文档是否具有相同判决结果，是确立其在法律检索中相关性的核心依据。然而，现有法律检索研究要么忽视了判决预测的重要作用，要么依赖隐式训练目标，期望法律文档能基于其判决结果在向量空间中实现合理对齐。这两种方法都未为相关性建模提供判决一致性的显式证据，导致检索结果不准确且缺乏透明度。为解决该问题，我们在生成式检索框架内提出了一种法律引导的方法GEAR。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。在两个中国法律案例检索数据集上的实验表明，GEAR在保持具有竞争力的判决预测性能的同时，效果优于现有最优方法。此外，我们在一个法国法律条文检索数据集上验证了其跨语言和跨领域的鲁棒性。
英文摘要：
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.
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