My research interests lie in the area of building a dialogue system to generate interesting and entertaining responses, with a particular focus on knowledge-grounded dialogue systems. Study of open-domain dialogue systems seeks to maximize user engagement by enhancing specific dialogue skills. To achieve this goal, much research has focused on the generation of empathetic responses, personality-based responses, and knowledge-grounded responses. In addition, interesting and entertaining responses from the open-domain dialogue systems can increase user satisfaction and engagement due to their diversity and ability to attract the user’s interest. It has also been observed in task-oriented dialogue, user engagement can be increased by incorporating interesting responses into the dialogue. For example, methods have been proposed to incorporate interesting responses into spoken dialogue systems (SDSs) that support the execution of complex tasks and provide a pleasant and enjoyable experience for the user. However, even in the case of interesting responses, if the dialogue is incoherent, user engagement is likely to be significantly reduced. To create a dialogue system that is consistent and interesting in a dialogue context, I am working on using knowledge-grounded response generation methods to select interesting knowledge that is relevant to the dialogue context and to make responses that are based on that knowledge.

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.