{"ID":6537687,"CreatedAt":"2026-07-14T02:54:43.516908796Z","UpdatedAt":"2026-07-15T03:28:55.185153975Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.11109","arxiv_id":"2607.11109","title":"Generative Chinese Statute Retrieval","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.","short_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...","url_abs":"https://arxiv.org/abs/2607.11109","url_pdf":"https://arxiv.org/pdf/2607.11109v1","authors":"[\"Yiteng Tu\",\"Zitao Su\",\"Weihang Su\",\"Xuanyi Chen\",\"Yueyue Wu\",\"Yiqun Liu\",\"Min Zhang\",\"Qingyao Ai\"]","published":"2026-07-13T05:38:50Z","proceeding":"cs.IR","tasks":"[\"cs.IR\",\"cs.CL\"]","methods":"[]","has_code":false}