GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval
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...
| Authors | Minghan Li, Tianrui Lv, Chao Zhang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 26 April 2026 |
| Last updated at source | 26 April 2026 |
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