Research on the Construction of Knowledge QA System Driven by Large Language Model: One Case Study of Power Transformer Domain
Hao Xu, Zhenyuan Kang, Yan Zhang, Zhenqiang Jin, Mulan Wang, Linlin Ge, Haihua Lu · 2024
The QA system can provide various professionals with fast and accurate knowledge support, significantly improving work efficiency. The introduction of LLMs has further enhanced the accuracy and efficiency of QA systems. A QA system based on LLMs for power transformers can effectively improve the efficiency of fault diagnosis and the resolution of technical issues in the power transformer domain. This study first proposes an automatic dataset construction method for QA based on LLMs, through which a QA dataset of basic knowledge about power transformers is obtained. This dataset then performs low-rank adaptive fine-tuning on the LLM. Subsequently, an external knowledge base covering fundamental knowledge and fault cases of power transformers is built. Finally, with the aid of prompt texts, knowledge responses were generated by RAG in combination with the fine-tuned LLM. Experimental results show that compared with the QA systems driven by general LLMs, the method proposed in this paper generates more concise and professional responses, thus promoting, to some extent, the application of LLMs in the power domain.