| Autor: |
XIANG Xiaowei, SHEN Yanguang, HU Minghao, YAN Tianwei, LUO Wei, LUO Zhunchen |
| Jazyk: |
čínština |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Jisuanji kexue yu tansuo, Vol 18, Iss 9, Pp 2349-2360 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1673-9418 |
| DOI: |
10.3778/j.issn.1673-9418.2406023 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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