User reviews on e-commerce and review sites are crucial for making purchase decisions, although creating detailed reviews is time-consuming and labor-intensive. In this study, we propose a novel use of dialogue systems to facilitate user review creation by generating reviews from information gathered during interview dialogues with users. To validate our approach, we implemented our system using GPT-4 and conducted comparative experiments from the perspectives of system users and review readers. The results indicate that participants who used our system rated their interactions positively. Additionally, reviews generated by our system required less editing to achieve user satisfaction compared to those by the baseline. We also evaluated the reviews from the readers’ perspective and found that our system-generated reviews are more helpful than those written by humans. Despite challenges with the fluency of the generated reviews, our method offers a promising new approach to review writing.

My research interests broadly lie in the influence of artificial intelligence (AI) agents on human decision-making. Specifically, I aim to develop applications for conversational agents in decision-making support. During my master’s program, I developed a system that uses an interview dialogue system to support user review writing. In this approach, the conversational agent gathers product information such as users’ impressions and opinions during the interview, to create reviews, facilitating the review writing process. Additionally, I conducted a comprehensive evaluation from the perspectives of system users and review readers. Although experimental results have shown that the system is capable of generating helpful reviews, the quality of the reviews still depends on how effectively the agent elicits the information from users. Therefore, I believe that personalizing the agent’s interview strategy to users’ preferences regarding the review writing process can further enhance both the user experience and the helpfulness of the review.

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.