基于大模型检索增强生成的计算机网络实验课程问答系统设计与实现  

Design and implementation of large language model retrieval-augmented generation-based computer network experiment course QA system

作　　者：张力军[1] 刘偲 廖纪童 高渝路 韩贺 张亮[1] 刘艳芳[1] ZHANG Lijun;LIU Si;LIAO Jitong;GAO Yulu;HAN He;ZHANG Liang;LIU Yanfang(School of Computer Science and Engineering,Beihang University,Beijing 100191,China;School of Artificial Intelligence,Beihang University,Beijing 100191,China)

机构地区：[1]北京航空航天大学计算机学院,北京100191 [2]北京航空航天大学人工智能学院,北京100191

出　　处：《实验技术与管理》2024年第12期186-192,共7页Experimental Technology and Management

基　　金：国家自然科学基金项目(U23B2010);北京航空航天大学研究生教育与发展研究专项基金项目(JG2024014)。

摘　　要：基于大模型检索增强生成的计算机网络实验课程问答系统的设计与实现,主要包括大语言模型、外挂知识库与规范性检测系统,通过大语言模型的通用性提供高度定制化的回答,通过外挂知识库实现数据条目的精细化管理,通过规范性检测系统保证系统的健壮性。还设计了微信小程序,用于信息展示与用户交互,提升系统的可用性。课堂问答测试结果表明,该系统能够准确回答学生遇到的问题,同时拒绝回答不合理、不安全的内容。该系统应用范围广、响应速度快、回答性能好,是大语言模型与实验课程教学相融合的成功尝试。[Objective]With the development of online course learning and online experimental teaching,numerous challenges to the online-offline blended teaching mode in computer network experiment courses have emerged.On one hand,online experiments meet the demand for students to conduct experiments anytime and anywhere;on the other hand,these experiments require immediate support and clarification for students.There are two main obstacles to overcome in this teaching mode.First,a lot of frequently asked questions related to common experimental issues are often raised,which leads to inefficient and limited human responses.Second,students often struggle to quickly locate experimental guidance within the abundant and complex reference materials,resulting in untimely solutions and delayed experimental progress,ultimately impacting the effectiveness of course learning.[Methods]A computer network experiment course QA system based on retrieval-augmented generation using a large language model is designed,comprising a large language model,an external knowledge base,and a normative detection system.The system utilizes the versatility of large language models to provide highly customized responses with relevant context from the external knowledge base,which consists of over 200,000 words from experiment-related materials,including textbooks,frequently asked questions,and chat history from the course WeChat group.The multilevel normative detection system includes word-level keyword retrieval and semantic-level large language model assessment,which rejects the generation of responses to problems not related to the course,thereby ensuring the system's robustness.To generate responses for specific questions,the system initially applies a large-language-model-based query enhancement to the original query,rewriting it into a similar query,and answers both queries with the large language model directly without any external knowledge.These query–answer pairs retrieve context from the external knowledge base using both BM25 and vector retr

关 键 词：大语言模型 课程问答系统 检索增强生成 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]