摘要
大语言模型是近年来自然语言处理领域的一个重大突破,已成为该领域研究的一种新范式。在金融、法律等垂直领域,基于FinGPT,ChatLaw等垂直领域大模型的智能问答系统,促进了大模型技术在相关领域的学术研究与应用落地。然而,由于电力领域缺乏相关的高质量数据,相关的大模型问答系统的构建工作遇到了较大阻碍。为了构建电力领域的智能问答系统,提出了基于大语言模型的电力知识库智能问答系统ChatPower。为了确保问答效果,ChatPower充分利用了电力管理各环节的数据。通过语义化理解,梳理和整合了大量的电力专业知识,精心设计和构建了一个较大规模的电力系统知识库。该知识库覆盖电力相关规章制度、安全生产管理体系以及发电设备故障知识等方面的内容。此外,通过参考检索到的电力知识,ChatPower显著缓解了问答中存在的模型幻觉问题,并在检索系统中引入了BM25检索、向量库检索与重排相结合的方法,有效降低了单纯依赖向量库检索的不准确性。同时,ChatPower结合基于大模型的提示工程技术,提升了对于规章制度类型问题生成回复的条理性。为了对问答系统进行评价,构建了一个电力知识问答的测试数据集,并对其进行了测试验证,测试结果表明:基于大语言模型的电力知识库问答系统ChatPower能够有效提升电力相关知识的检索和问答的准确性。
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.
作者
张金营
王天堃
么长英
谢华
柴林政
刘书恺
李彤亮
李舟军
ZHANG Jinying;WANG Tiankun;YAO Changying;XIE Hua;CHAI Linzheng;LIU Shukai;LI Tongliang;LI Zhoujun(Guodian Power Development Co.,Ltd.,Dalian,Liaoning 116000,China;Guoneng Xinkong Internet Technology Co.,Ltd.,Beijing 100039,China;School of Computer Science and Engineering,Beihang University,Beijing 100191,China;Computer School,Beijing Information Science and Technology University,Beijing 102206,China)
出处
《计算机科学》
CSCD
北大核心
2024年第12期286-292,共7页
Computer Science
基金
国家自然科学基金(62276017)
软件开发环境国家重点实验室自主研究项目(SKLSDE-2021ZX-18)。
关键词
大语言模型
知识库问答系统
信息检索
自然语言生成
Large language model
Knowledge base question answering system
Information retrieval
Natural language generation
