@inproceedings{na-etal-2025-survey,
title = "A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions",
author = "Na, Hongbin and
Hua, Yining and
Wang, Zimu and
Shen, Tao and
Yu, Beibei and
Wang, Lilin and
Wang, Wei and
Torous, John and
Chen, Ling",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.385/",
doi = "10.18653/v1/2025.findings-acl.385",
pages = "7362--7376",
ISBN = "979-8-89176-256-5",
abstract = "Mental health is increasingly critical in contemporary healthcare, with psychotherapy demanding dynamic, context-sensitive interactions that traditional NLP methods struggle to capture. Large Language Models (LLMs) offer significant potential for addressing this gap due to their ability to handle extensive context and multi-turn reasoning. This review introduces a conceptual taxonomy dividing psychotherapy into interconnected stages{--}assessment, diagnosis, and treatment{--}to systematically examine LLM advancements and challenges. Our comprehensive analysis reveals imbalances in current research, such as a focus on common disorders, linguistic biases, fragmented methods, and limited theoretical integration. We identify critical challenges including capturing dynamic symptom fluctuations, overcoming linguistic and cultural biases, and ensuring diagnostic reliability. Highlighting future directions, we advocate for continuous multi-stage modeling, real-time adaptive systems grounded in psychological theory, and diversified research covering broader mental disorders and therapeutic approaches, aiming toward more holistic and clinically integrated psychotherapy LLMs systems."
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<abstract>Mental health is increasingly critical in contemporary healthcare, with psychotherapy demanding dynamic, context-sensitive interactions that traditional NLP methods struggle to capture. Large Language Models (LLMs) offer significant potential for addressing this gap due to their ability to handle extensive context and multi-turn reasoning. This review introduces a conceptual taxonomy dividing psychotherapy into interconnected stages–assessment, diagnosis, and treatment–to systematically examine LLM advancements and challenges. Our comprehensive analysis reveals imbalances in current research, such as a focus on common disorders, linguistic biases, fragmented methods, and limited theoretical integration. We identify critical challenges including capturing dynamic symptom fluctuations, overcoming linguistic and cultural biases, and ensuring diagnostic reliability. Highlighting future directions, we advocate for continuous multi-stage modeling, real-time adaptive systems grounded in psychological theory, and diversified research covering broader mental disorders and therapeutic approaches, aiming toward more holistic and clinically integrated psychotherapy LLMs systems.</abstract>
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%0 Conference Proceedings
%T A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions
%A Na, Hongbin
%A Hua, Yining
%A Wang, Zimu
%A Shen, Tao
%A Yu, Beibei
%A Wang, Lilin
%A Wang, Wei
%A Torous, John
%A Chen, Ling
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F na-etal-2025-survey
%X Mental health is increasingly critical in contemporary healthcare, with psychotherapy demanding dynamic, context-sensitive interactions that traditional NLP methods struggle to capture. Large Language Models (LLMs) offer significant potential for addressing this gap due to their ability to handle extensive context and multi-turn reasoning. This review introduces a conceptual taxonomy dividing psychotherapy into interconnected stages–assessment, diagnosis, and treatment–to systematically examine LLM advancements and challenges. Our comprehensive analysis reveals imbalances in current research, such as a focus on common disorders, linguistic biases, fragmented methods, and limited theoretical integration. We identify critical challenges including capturing dynamic symptom fluctuations, overcoming linguistic and cultural biases, and ensuring diagnostic reliability. Highlighting future directions, we advocate for continuous multi-stage modeling, real-time adaptive systems grounded in psychological theory, and diversified research covering broader mental disorders and therapeutic approaches, aiming toward more holistic and clinically integrated psychotherapy LLMs systems.
%R 10.18653/v1/2025.findings-acl.385
%U https://aclanthology.org/2025.findings-acl.385/
%U https://doi.org/10.18653/v1/2025.findings-acl.385
%P 7362-7376
Markdown (Informal)
[A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions](https://aclanthology.org/2025.findings-acl.385/) (Na et al., Findings 2025)
ACL
- Hongbin Na, Yining Hua, Zimu Wang, Tao Shen, Beibei Yu, Lilin Wang, Wei Wang, John Torous, and Ling Chen. 2025. A Survey of Large Language Models in Psychotherapy: Current Landscape and Future Directions. In Findings of the Association for Computational Linguistics: ACL 2025, pages 7362–7376, Vienna, Austria. Association for Computational Linguistics.