Construction and Evaluation of Intelligent Question Answering System for Electric Power Knowledge Base Based on Large Language Model

Large language model is a major breakthrough in the field of natural language processing in recent years and have become a new paradigm for research in this field.In vertical fields such as finance and law,intelligent question and answering systems based on large models in vertical fields such as FinGPT and ChatLaw have promoted the academic research and application of large model technology in related fields.However,due to the lack of relevant high-quality data in the electric power field,the construction of related large-model question answering systems has encountered great obstacles.In order to build an intelligent question and answering system in the electric power field,an intelligent question and answering system for electric power know-ledge base ChatPower based on a large language model is proposed.In order to ensure the Q&A effect,ChatPower fully utilizes data from all aspects of power management,sorts out and integrates a large amount of power professional knowledge through semantic understanding,and carefully designs and constructs a large-scale power system knowledge base.The knowledge base co-vers power-related rules and regulations,production safety management systems,and power generation equipment failure know-ledge.In addition,by referring to the retrieved electricity knowledge,ChatPower significantly reduces the problem of model illusion in question and answering,and introduces a method that combines BM25 retrieval,dense retrieval and rerank in the retrieval system,effectively reducing the the inaccuracy of relying solely on vector library retrieval.At the same time,ChatPower combines prompt engineering technology based on large models to improve the orderliness of generating responses to rules and regulations type questions.In order to evaluate the Q&A system,a test data set for electric power knowledge question and answering is constructed,and ChatPower is tested and verified.The test results show that the electric power knowledge base question and answe-ring system ChatPower based on a large language model can effectively improve the accuracy of retrieval of power-related know-ledge and Q&A.