Research on Science and Technology Policy and Regulation Q&A System Driven by Large Models

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 significantly enhance the accuracy and efficiency of such systems. However, current LLM-based S&T policy and regulation 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 professional 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. Furthermore, 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.

Keywords

• large language model; question-and-answer dataset; low-rank adaptive fine-tuning; prompt learning; science and technology policy and regulation; question-and-answer system