Can LLMs and humans be friends? Uncovering factors affecting human-AI intimacy formation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866915313666228224 |
|---|---|
| author | Hong, Yeseon Choi, Junhyuk Kim, Minju Kim, Bugeun |
| author_facet | Hong, Yeseon Choi, Junhyuk Kim, Minju Kim, Bugeun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_24658 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Can LLMs and humans be friends? Uncovering factors affecting human-AI intimacy formation Hong, Yeseon Choi, Junhyuk Kim, Minju Kim, Bugeun Human-Computer Interaction H.5.2; I.2.7 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. |
| title | Can LLMs and humans be friends? Uncovering factors affecting human-AI intimacy formation |
| topic | Human-Computer Interaction H.5.2; I.2.7 |
| url | https://arxiv.org/abs/2505.24658 |