Can LLMs and humans be friends? Uncovering factors affecting human-AI intimacy formation

TL;DR

This study investigates how factors like self-disclosure, reciprocity, and naturalness influence the development of social intimacy between humans and large language models, revealing that gradual self-disclosure and natural responses enhance perceived closeness.

Contribution

It provides empirical evidence on the roles of self-disclosure and response naturalness in fostering human-LLM intimacy, highlighting the importance of response calibration.

Findings

Gradual self-disclosure significantly increases perceived intimacy.

Self-criticism methods produce more natural and intimate responses.

Excessive empathetic expressions can disrupt user immersion.

Abstract

Large language models (LLMs) are increasingly being used in conversational roles, yet little is known about how intimacy emerges in human-LLM interactions. Although previous work emphasized the importance of self-disclosure in human-chatbot interaction, it is questionable whether gradual and reciprocal self-disclosure is also helpful in human-LLM interaction. Thus, this study examined three possible aspects contributing to intimacy formation: gradual self-disclosure, reciprocity, and naturalness. Study 1 explored the impact of mutual, gradual self-disclosure with 29 users and a vanilla LLM. Study 2 adopted self-criticism methods for more natural responses and conducted a similar experiment with 53 users. Results indicate that gradual self-disclosure significantly enhances perceived social intimacy, regardless of persona reciprocity. Moreover, participants perceived utterances generated with self-criticism as more natural compared to those of vanilla LLMs; self-criticism fostered higher intimacy in early stages. Also, we observed that excessive empathetic expressions occasionally disrupted immersion, pointing to the importance of response calibration during intimacy formation.