基于检索增强生成的智能问答系统关键技术研究

Research on the Key Technologies for Intelligent Q&A System Based on Retrieval Augmented Generation Technology

大语言模型(LLM)能高效理解和处理复杂语义问题,在智能对话系统中得到广泛应用。然而,在结果生成的准确性和时效性上,LLM仍受到限制。检索增强生成(RAG)通过检索海量文档、网站或数据库等电子资源,将获取的信息融入LLM生成文本中,为智能对话系统处理复杂问题时提供更准确、更深入的答案,可有效提高对话系统的准确性和时效性。文章通过概述智能问答系统发展源流、RAG深度学习核心原理,深入研究该系统构建过程(数据索引、文档检索、文本生成)及其中涉及的关键技术(语义表示、大语言模型研究、提示工程),以期为智能问答系统的进一步开发与应用提供参考。

Large Language Models(LLMs)can efficiently understand and process complex semantic problems and are widely used in intelligent dialog systems.However,LLMs are still limited in terms of accuracy and timeliness of result generation.Retrieval Augmented Generation(RAG)technology can effectively improve the accuracy and timeliness of dialog systems by retrieving electronic resources such as massive documents,websites or databases,and incorporating the acquired information into LLM-generated text,which provides more accurate and in-depth answers for intelligent dialog systems to deal with complex questions.The paper provides an overview of the development history of intelligent question-answering systems,the core principles of RAG deep learning,and delves into the construction process of such systems〈data indexing,document retrieval,text generation〉as well as the key technologies involved〈semantic representation,research on LLMs,prompt engineering〉,with the aim of providing references for the further development and application of intelligent question-answering systems..