Application and Optimization of Large Language Model in the Construction of Electric Power Security Corpus

Abstract

Abstract: This paper focuses on the application and optimization of the Large Language Model (LLM) in the construction of a power safety corpus. The opening chapter describes the importance of electric power security, the challenges it faces, and the concept and application of LLM, pointing out the significance, content and structure of studying the application of LLM in this field. Then, it analyzes the foundation of LLM in depth, introducing its definition, principle, characteristic advantages and typical models. The current situation of power security corpus construction is discussed in detail, including current construction methods and challenges. Focus on the application of LLM in the construction of power security corpus, such as data preprocessing, corpus annotation and corpus optimization. Strategies such as security and privacy protection, performance improvement, semantic understanding and interaction optimization are proposed to address the problems in the application. Finally, we look forward to the development prospect of LLM in the field of electric power security and provide directions for subsequent research. The study shows that LLM has significant potential in the construction of power security corpus, and the optimization strategy can effectively solve the application problems and promote the intelligent development of power security field.