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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2509.12102 |
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| _version_ | 1866911155656589312 |
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| author | Wang, Synthia Cheng, Yuwei Song, Austin Keedy, Sarah Berman, Marc Feamster, Nick |
| author_facet | Wang, Synthia Cheng, Yuwei Song, Austin Keedy, Sarah Berman, Marc Feamster, Nick |
| contents | Limited access to mental health care has motivated the use of digital tools and conversational agents powered by large language models (LLMs), yet their quality and reception remain unclear. We present a study comparing therapist-written responses to those generated by ChatGPT, Gemini, and Llama for real patient questions. Text analysis showed that LLMs produced longer, more readable, and lexically richer responses with a more positive tone, while therapist responses were more often written in the first person. In a survey with 150 users and 23 licensed therapists, participants rated LLM responses as clearer, more respectful, and more supportive than therapist-written answers. Yet, both groups of participants expressed a stronger preference for human therapist support. These findings highlight the promise and limitations of LLMs in mental health, underscoring the need for designs that balance their communicative strengths with concerns of trust, privacy, and accountability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12102 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Can LLMs Address Mental Health Questions? A Comparison with Human Therapists Wang, Synthia Cheng, Yuwei Song, Austin Keedy, Sarah Berman, Marc Feamster, Nick Human-Computer Interaction Artificial Intelligence Limited access to mental health care has motivated the use of digital tools and conversational agents powered by large language models (LLMs), yet their quality and reception remain unclear. We present a study comparing therapist-written responses to those generated by ChatGPT, Gemini, and Llama for real patient questions. Text analysis showed that LLMs produced longer, more readable, and lexically richer responses with a more positive tone, while therapist responses were more often written in the first person. In a survey with 150 users and 23 licensed therapists, participants rated LLM responses as clearer, more respectful, and more supportive than therapist-written answers. Yet, both groups of participants expressed a stronger preference for human therapist support. These findings highlight the promise and limitations of LLMs in mental health, underscoring the need for designs that balance their communicative strengths with concerns of trust, privacy, and accountability. |
| title | Can LLMs Address Mental Health Questions? A Comparison with Human Therapists |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2509.12102 |