让法律检索理解真实案情逻辑，用生成推理提升匹配准确率。
GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval
- 将检索任务转为法律要素生成与推理过程，增强可解释性。
- 在LeCaRD数据集上超越基线模型，10%数据训练仍保持高精度。
- 适合法律AI研究者、智能裁判辅助系统开发者使用。
自然语言查询与专业法律文书之间的语义鸿沟是法律案例检索（LCR）的核心挑战。现有稠密检索方法通常将LCR视为黑箱语义匹配过程，忽视了法律相关性背后的显式法理逻辑。为此，我们提出GLIER（生成式法律推理与证据排序框架），将检索重构为对潜在法律变量的推理过程。GLIER将任务分解为两个可解释性驱动阶段：首先，联合生成推理模块采用统一的序列到序列策略，将原始查询转化为包括罪名和法律要件在内的潜在法律指标，并通过联合生成确保逻辑一致性；其次，多视角证据融合机制整合生成置信度、结构化与词汇信号实现精准排序。在LeCaRD和LeCaRDv2上的大量实验表明，GLIER显著优于SAILER、KELLER等强基线模型。尤为突出的是，GLIER展现出优异的数据效率，在仅使用10%训练数据时仍保持稳健性能。
原文摘要 · Abstract (English)
The semantic gap between colloquial user queries and professional legal documents presents a fundamental challenge in Legal Case Retrieval (LCR). Existing dense retrieval methods typically treat LCR as a black-box semantic matching process, neglecting the explicit juridical logic that underpins legal relevance. To address this, we propose GLIER (Generative Legal Inference and Evidence Ranking), a framework that reformulates retrieval as an inference process over latent legal variables. GLIER decomposes the task into two interpretability-driven stages. First, a Joint Generative Inference module translates raw queries into latent legal indicators, including charges and legal elements, using a unified sequence-to-sequence strategy that jointly generates charges and elements to enforce logical consistency. Second, a Multi-View Evidence Fusion mechanism aggregates generative confidence with structural and lexical signals for precise ranking. Extensive experiments on LeCaRD and LeCaRDv2 demonstrate that GLIER outperforms strong baselines such as SAILER and KELLER. Notably, GLIER exhibits strong data efficiency, maintaining robust performance even when trained with only 10% of the data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。