基于相向信息增强学习的法条检索OA

To address the expression discrepancy between users'vague queries and the professionalism of statutes in statute retrieval,this paper proposes a bidirectional legal information enhancement learning method.By reducing the information gap between que-ries and statutes,this approach eases the model's learning difficulty and improves retrieval performance.On the query side,large lan-guage models are utilized to generate explanatory answers,supplementing missing legal concepts and reasoning logic.On the statute side,an incremental representation is built that incorporates law names,structures,and contents,enhancing the semantic richness of legal texts.Furthermore,by integrating both enhanced query and statute information,the paper designs a hierarchical legal informa-tion-aware contrastive learning framework,enabling precise semantic mapping between user queries and statutes.Experimental re-sults demonstrate that the proposed method significantly improves retrieval performance,increasing Recall@5 from 52.90%to 69.74%and MRR@3 from 47.12%to 63.80%,offering high-quality statute retrieval services for both legal practitioners and large language models.