标准化学报Issue(8)：63-71,9.DOI:10.3969/j.issn.2097-857X.2026.08.008
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
Application of Large Language Model Classification Technology Combining Contrastive Learning and Multi-task Learning in Standard Retrieval System
摘要
Abstract
[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.关键词
分类/对比学习/多任务学习/CCSKey words
classification/contrastive learning/multi-task learning/CCS引用本文复制引用
张勇,王益谊,于钢,李娟,张阳..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[J].标准化学报,2026,(8):63-71,9.基金项目
本文受中国标准化研究院基本科研业务费项目"面向大模型的结构化XML标准文档可读可理解工具研发"(项目编号:252025Y-12532)资助. （项目编号:252025Y-12532）
