My research interest involves persona dialogue systems, which use the profile information of a character or real person, called a persona, and responds accordingly. Persona dialogue systems can improve the consistency of the system’s responses, users’ trust, and user enjoyment. My current research focuses on persona dialogue systems, especially dialogue agents that role-play as fictional characters. The first task involves obtaining the dialogue and personas of novel characters and building a dialogue corpus. The second task involves evaluating whether the dialogue agent’s responses are character-like relative to the context. The goal of these studies is to allow dialogue agents to generate responses that are more character-like.

The Werewolf Game is a communication game where players’ reasoning and discussion skills are essential. In this study, we present a Werewolf AI agent developed for the AIWolfDial 2024 shared task, co-hosted with the 17th INLG. In recent years, large language models like ChatGPT have garnered attention for their exceptional response generation and reasoning capabilities. We thus develop the LLM-based agents for the Werewolf Game. This study aims to enhance the consistency of the agent’s utterances by utilizing dialogue summaries generated by LLMs and manually designed personas and utterance examples. By analyzing self-match game logs, we demonstrate that the agent’s utterances are contextually consistent and that the character, including tone, is maintained throughout the game.