计算机与现代化Issue(7)：12-18,7.DOI:10.3969/j.issn.1006-2475.2026.07.002
大语言模型提示词迭代的低成本查询词理解方法
Low-cost Query Term Understanding Method Based on Iterative Prompting of Large Language Models
摘要
Abstract
To address the high cost and development barriers associated with query term understanding in natural language un-derstanding tasks,this paper proposes a low-cost query term understanding method based on iterative prompting of Large Lan-guage Models(LLMs).Aiming at the query classification task in search engines,the method uses LLMs to construct initial prompt words and guide the large model to optimize the prompt words through multiple rounds,achieving automated high-quality annotation of large-scale training data.Then,through knowledge distillation,the capabilities are transferred to small models to meet the application requirements of real-time performance and resource-constrained scenarios.The experimental results show that,under the optimal prompt ensemble,the LLMs achieves a precision of 97.5％and an accuracy of 93.0％in query classifica-tion.This method significantly reduces reliance on manual annotation,cutting the labeling cost for 70000 samples from 21000 RMB(manual labeling)to only 30.24 RMB(LLMs-based labeling),greatly improving both data annotation efficiency and model development efficiency.Further,through knowledge distillation,the capabilities of large models are transferred to small models,maintaining high classification performance while significantly enhancing the deployment flexibility of the models.Therefore,this method provides a feasible,economical and efficient technical path for text classification tasks in low-resource environments.关键词
查询词理解/大语言模型/提示词优化/知识蒸馏/文本分类Key words
query term understanding/large language models/prompt optimization/knowledge distillation/text classification分类
信息技术与安全科学引用本文复制引用
张军,张红梅,王石磊..大语言模型提示词迭代的低成本查询词理解方法[J].计算机与现代化,2026,(7):12-18,7.基金项目
国家自然科学基金资助项目(62162002) （62162002）
