A Shogi Dialogue System That Collaborates with and Entertains the User

Abstract

Recent advances in Large Language Models (LLMs) have made it possible to generate natural and diverse commentary in board games such as chess and shogi. However, existing commentary systems for these games often produce mechanically phrased explanations, lacking emotional richness and the sense of companionship that arises when playing with a friend.In this study, we focus on shogi and aim to develop a shogi dialogue system that enables users to engage in more natural, human-like interactions while playing. The proposed system is designed to provide a graphical interface, extract multifaceted features from game states (such as SFEN representation, legal moves, engine evaluations with depth-dependent variations, reading lines, and piece influence), and combine them with a commentary dataset constructed from game records for generating commentary responses. By fine-tuning LLMs and designing prompts that incorporate uncertainty, surprise, and emotional expressions, the system seeks to generate responses that are not only analytical but also emotionally engaging. We evaluate whether such responses enhance entertainment value and user engagement compared to conventional commentary systems.