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Legal case retrieval and judgment prediction are crucial components inintelligent legal systems. In practice, determining whether two cases share thesame charges through legal judgment prediction is essential for establishingtheir relevance in case retrieval. However, current studies on legal caseretrieval merely focus on the semantic similarity between paired cases,ignoring their charge-level consistency. This separation leads to a lack ofcontext and potential inaccuracies in the case retrieval that can underminetrust in the system's decision-making process. Given the guidance role of lawsto both tasks and inspired by the success of generative retrieval, in thiswork, 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 keyelements) for legal cases according to the definition of charges in laws,ensuring a shared and informative representation for both tasks. Then inaccordance with the inherent hierarchy of laws, we construct a law structureconstraint tree and assign law-aware semantic identifier(s) to each case basedon this tree. These designs enable a unified traversal from the root, throughintermediate charge nodes, to case-specific leaf nodes, which respectivelycorrespond to two tasks. Additionally, in the training, we also introduce arevision loss that jointly minimizes the discrepancy between the identifiers ofpredicted and labeled charges as well as retrieved cases, improving theaccuracy and consistency for both tasks. Extensive experiments on two datasetsdemonstrate that Gear consistently outperforms state-of-the-art methods inlegal case retrieval while maintaining competitive judgment predictionperformance.

Incorporating Judgment Prediction into Legal Case Retrieval via Law-aware Generative Retrieval