[3Win5] Poster session 3

[3Win5-54] Development of a Conversational Memory System for AI Chatbots Using LLMs

〇Yuki Takei¹, Takahiro Ikeuchi¹, Airi Oda¹, Taro Uchida¹, Hikari N Takashina¹ (1.Awarefy Inc.)

Keywords:LLM, Chatbot, ChatGPT, Long-Term Memory

As the development of services utilizing large-scale language models (LLMs) continues, there is growing interest in the use of AI technology in the mental health field as well. We have been developing a chatbot using LLM as part of a service to support individual self-care. However, there are issues such as the lack of consistent conversation and personalized responses. In this study, we summarized the issues of the current chatbot based on the opinions received from users and developed a memory system to realize “responses utilizing past conversations” among them. The memory system implemented in this study enabled the generation of suggestions, topics, and answers to user questions based on the content of past conversations. This paper describes the design and implementation of the memory function using a large-scale language model and shows an example response.