GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval

ACL ARR 2026 January Submission10686 Authors

Keywords: Legal Case Retrieval, Information Retrieval

Abstract: 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: (1) A Joint Generative Inference module that translates raw queries into latent legal indicators (Charges and Legal Elements), employing a unified sequence-to-sequence strategy where charges and elements are generated jointly to enforce logical consistency; and (2) A Multi-View Evidence Fusion mechanism that aggregates generative confidence with structural and lexical signals for precise ranking. Extensive experiments on LeCaRD and LeCaRDv2 demonstrate that GLIER outperforms strong baselines like SAILER and KALLER. Notably, our framework exhibits exceptional data efficiency, maintaining robust performance even when trained with only 10\% of the data.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: legal NLP, re-ranking

Contribution Types: NLP engineering experiment

Languages Studied: Chinese

Submission Number: 10686