Research on Intelligent Question-Answering Systems Based on Large Language Models and Knowledge Graphs

Abstract

With the continuous development of artificial intelligence and cloud computing technologies, the emergence of large language models (LLMs) has created new opportunities for intelligent applications. However, large language models may lack authenticity and accuracy when providing answers in specific professional domains, and they even generate "illusory facts." In response to the limitations of current large language models in solving specific professional fields, this paper proposes to use large language models and knowledge graph technology to construct an intelligent question answering system for specific fields. Through systematic training and optimization, efficient domain specific knowledge Q&A has been achieved, improving the satisfaction rate of domain specific knowledge Q&A. The intelligent question answering system based on large models and knowledge graphs brings more convenience to people's lives and work, which is beneficial for users to obtain intelligent solutions in fields such as education, healthcare, and customer service.