Can LLMs Address Mental Health Questions? A Comparison with Human Therapists
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 6 of its 52 references that are in the map. First posted on arXiv 15 Sep 2025.
Builds on
6 of 52 references.
References · 6
- 2023Understanding the Benefits and Challenges of Using Large Language Model-based Conversational Agents for Mental Well-being Supportcited by 75
- 2023Evaluation of ChatGPT for NLP-based Mental Health Applicationscited by 53
- 2023Rethinking Large Language Models in Mental Health Applicationscited by 16
- 2025Exploring User Security and Privacy Attitudes and Concerns Toward the Use of General-Purpose LLM Chatbots for Mental Healthcited by 13
- 2024MindGuard: Towards Accessible and Sitgma-free Mental Health First Aid via Edge LLMcited by 7
- 2025Designing an LLM-Based Behavioral Activation Chatbot for Young People with Depression: Insights from an Evaluation with Artificial Users and Clinical Expertscited by 1
Built on by
Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2025MindEval: Benchmarking Language Models on Multi-turn Mental Health Supportcited by 10
- 2026PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethicscited by 4
- 2026LLMs Should Incorporate Explicit Mechanisms for Human Empathycited by 0
- 2026Psy-Chronicle:A Structured Pipeline for Synthesizing Long-Horizon Campus Psychological Counseling Dialoguescited by 0
- 2026Psy-Chronicle:A Structured Pipeline for Synthesizing Long-Horizon Campus Psychological Counseling Dialogues21 May 2026
- 2026LLMs Should Incorporate Explicit Mechanisms for Human Empathy12 Apr 2026
- 2026PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics7 Jan 2026
- 2025MindEval: Benchmarking Language Models on Multi-turn Mental Health Support23 Nov 2025