Acquisition of Utterance Intent and Construction of Empathetic Dialogue System Using Japanese Large Language Model

This paper proposes a novel framework for empathetic dialogue generation in Japanese large language models (LLMs) by employing LoRA-based adapter tuning. The proposed system incorporates both cognitive and emotional empathy through two key modules: the Next Intent Predictor and the Intent Combiner. These modules dynamically predict, select, and integrate multiple intent representations, enabling empathetic and user-centered responses tailored to the context. Evaluation results highlight significant improvements, including a notable increase in user-friendliness and empathetic quality compared to the base model, achieving a win rate exceeding 70%. Furthermore, the system demonstrates human-level competitiveness, with over 40% of its responses being rated as comparable to human-generated ones. This study demonstrates the effectiveness of combining reinforcement learning with intent-specific fine-tuning to enhance empathetic capabilities in dialogue systems. By focusing on intent-driven responses, this approach paves the way for more user-centric applications, particularly in privacy-sensitive and culturally nuanced contexts.