Embedding Psychological Distance Awareness into LLM-Based Dialogue Systems

Abstract

Psychological distance plays a fundamental role in human conversational dynamics, influencing linguistic style, perceived intimacy, and interaction quality. Despite recent advances in dialogue generation, most existing systems primarily focus on response fluency and coherence, while adaptive modeling of interpersonal distance remains largely underexplored. This paper presents a text-based dialogue framework that estimates the psychological distance between a user and the system solely from user utterances and adaptively adjusts response styles accordingly. The proposed method consists of three stages: (1) estimation of psychological distance from textual input, (2) conditioning of response generation on the estimated distance, and (3) stylistic adjustment of responses to align with the inferred interpersonal relationship. Psychological distance is modeled as a binary category (“close” vs. “distant”) based on politeness-theory-inspired linguistic criteria. To evaluate the approach, we conducted a user study in which participants engaged in dialogues under three interaction conditions: close, distant, and neutral. Both dialogue log analysis and subjective questionnaire evaluations were performed. Linguistic adaptation effects were analyzed using politeness, lexical, and response-length metrics, while subjective assessments measured conversational ease, perceived human-likeness, perceived distance, and willingness for further interaction. Results indicate that interactions under the close condition achieved higher ratings in conversational ease, perceived human-likeness, and engagement, and that stylistic linguistic adaptations observed in dialogue logs were associated with these subjective improvements.

Keywords: Psychological distance, dialogue systems, conversational style adaptation, user experience, human–AI interaction, text-based interaction

DOI: 10.54941/ahfe1007208