基于大语言模型的开放域对话系统综述OA

Open-domain dialogue systems aim to facilitate smooth and natural communication with users,simulating human language behavior.They can handle conversations on a variety of topics and contexts without being limited to specific tasks or domains.The goal is to enable computer systems to understand and generate natural language,thereby achieving efficient human-computer interaction.Large language models are deep learning models trained on vast amounts of text data.By learning the statistical properties of language and semantic relationships,they are capable of generating and understanding text,as well as reasoning.These models have become core technologies in open-domain dialogue systems.This paper reviews the current state of research on open-domain dialogue systems based on large language models,introduces commonly used dialogue datasets,and compares the characteristics and applications of different model architectures.An evolutionary architecture of dialogue systems is proposed,including a core capability layer,a function extension layer,and a dynamic optimization layer.The core capability layer ensures the continuity and consistency of dialogue through memory mechanisms and coherence assurance algorithms,providing a stable operating foundation for the system.On this basis,the function extension layer improves the richness and personalization of dialogue through external knowledge integration and personalized responses.The dynamic optimization layer ensures the stability and secu-rity of dialogue through the synergy of topic control and security constraints,and avoids dialogues from deviating from the topic or generating inappropriate content.The three-layer architecture cooperates with each other to promote the coor-dinated evolution of the dialogue system from basic capabilities to advanced functions,and summarizes the corresponding solutions.Based on existing research,the future development trends of the field are discussed.