计算机科学与探索2024，Vol.18Issue(9)：2349-2360,12.DOI:10.3778/j.issn.1673-9418.2406023
大模型驱动的科技政策法规问答系统研究
Research on Science and Technology Policy and Regulation Q&A System Driven by Large Models
摘要
Abstract
A question-and-answer(Q&A)system for science and technology(S&T)policies and regulations plays a critical role in helping the public understand and apply these regulations.Large language models(LLM)can signifi-cantly enhance the accuracy and efficiency of such systems.However,current LLM-based S&T policy and regula-tion Q&A systems face several challenges:the lack of large-scale,high-quality datasets,insufficient methods for auto-matically constructing datasets with accurate policy and regulation knowledge integration,and issues with the pro-fessional accuracy and timeliness of the models'knowledge updates.To address these challenges,this paper proposes a retrieval-augmented self-prompting method for constructing a high-quality,large-scale S&T policy and regulation Q&A dataset.Additionally,a Q&A system is developed,which combines an LLM optimized by low-rank adaptation(LoRA)techniques with an S&T policy and regulation knowledge base,and employs prompt learning techniques to guide the system in generating accurate answers.Experimental results demonstrate that the constructed Q&A dataset significantly improves the integration of policy and regulation knowledge compared with traditional methods.Fur-thermore,the proposed Q&A system outperforms general LLM-driven systems across various metrics,highlighting its enhanced performance in the domain of S&T policies and regulations.关键词
大语言模型/问答数据集/低秩自适应微调/提示学习/科技政策法规/问答系统Key words
large language model/question-and-answer dataset/low-rank adaptive fine-tuning/prompt learning/science and technology policy and regulation/question-and-answer system分类
信息技术与安全科学引用本文复制引用
向小伟,申艳光,胡明昊,闫天伟,罗威,罗准辰..大模型驱动的科技政策法规问答系统研究[J].计算机科学与探索,2024,18(9):2349-2360,12.基金项目
国家自然科学基金面上项目(62376284). This work was supported by the National Natural Science Foundation of China(62376284). （62376284）
