结合对比学习和多任务学习的大语言模型分类技术在标准检索系统中的应用OA

[Objective]When processing user queries,a standard keyword-based full-text retrieval system needs to perform retrieval over the full dataset.[Methods]To address this problem,this paper proposes a classification approach that combines contrastive learning and multi-task learning to map user queries into the Chinese Classification for Standards(CCS)taxonomy,thereby reducing the retrieval scope.A hierarchy-aware contrastive learning strategy and a knowledge-injected multi-task learning framework are further introduced to enhance the proposed method's ability to classify fine-grained categories within the CCS taxonomy.[Results]The proposed method achieves a Micro-F1 of 89.23%on the user query classification task;after being deployed in an online standard retrieval setting,Recall@5 and MRR increase by 10.3%and 9.5%,respectively.[Conclusion]Extensive experiments demonstrate that the proposed method effectively improves standard full-text retrieval systems.