用生成模型让法律查询更懂人话，精准匹配法条
Generative Chinese Statute Retrieval
- 将法条检索转为文本生成任务，内化法律知识
- 多粒度文档编码+多任务训练，显著提升检索效果
- 适合法律AI研发者和智能司法系统构建者
法条检索是法律信息检索的基础任务，但现有方法难以弥合口语化法律查询与正式法条语言之间的差距。本文提出GCSR，一种生成式法条检索框架，将法条检索重构为序列生成问题，并将法条知识内化至生成模型中。具体而言，我们设计了多粒度结构化docid，以编码法律层级与语义信息，并采用多任务训练策略。实验表明，GCSR在多个基准上持续优于强大多样检索基线（包括稀疏、稠密及法律领域模型）。结果验证了生成式检索在法条检索中的有效性，凸显其在更广泛的法律信息获取与下游法律推理任务中的潜力。
原文摘要 · Abstract (English)
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.
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