Classification of Properties in Human-like Dialogue Systems Using Generative AI to Adapt to Individual Preferences - Kaori Abe; Changqin Quan; Sheng Cao; Zhiwei Luo (Applied Sciences)
As the linguistic capabilities of AI-based dialogue systems improve, their human-likeness is increasing, and their behavior no longer receives a universal evaluation. To better adapt to users, the consideration of individual preferences is required. In this study, the relationships between the properties of a human-like dialogue system and dialogue evaluations were investigated using hierarchical cluster analysis for individual subjects. The dialogue system driven by generative AI communicated with subjects in natural language via voice-based communication and featured a facial expression function. Subjective evaluations of the system and dialogues were conducted through a questionnaire. Based on the analysis results, the system properties were classified into two types: generally and individually relational to a positive evaluation of the dialogue. The former included inspiration, a sense of security, and collaboration, while the latter included a sense of distance, personality, and seriousness. Equipping the former properties is expected to improve dialogues for most users. The latter properties should be adjusted to individuals since they are evaluated based on individual preferences. A design approach in accordance with individuality could be useful for making human-like dialogue systems more comfortable for users.