Generative Chinese Statute Retrieval
Yiteng Tu, Zitao Su, Weihang Su, Xuanyi Chen, Yueyue Wu, Yiqun Liu, Min Zhang, Qingyao Ai
Abstract
Statute retrieval is a fundamental task in legal information retrieval, yet existing approaches struggle to bridge the gap between colloquial legal queries and formal statutory language. In this paper, we propose GCSR, a generative statute retrieval framework that reformulates statute retrieval as a sequence generation problem and internalizes statutory knowledge into a generative model. Specifically, we propose a multi-granularity structured docid that encodes legal hierarchy and semantic information, together with a multi-task training strategy. Experiments show that GCSR consistently outperforms strong sparse, dense, and legal-domain baselines. Our results demonstrate the effectiveness of generative retrieval for statute retrieval and highlight its potential for broader legal information access and downstream legal reasoning tasks.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.