Year: 2026
Type: conference-paper
Abstract: Applying large language models (LLMs) to assist in psycho-counseling is an emerging and meaningful approach, driven by the significant gap between patient needs and the availability of mental health support.However, current LLMs struggle to consistently provide effective responses to client speeches, largely due to the lack of supervision from highquality real psycho-counseling data, whose content is typically inaccessible due to client privacy concerns.Furthermore, the quality of therapists' responses in available sessions can vary significantly based on their professional training and experience.Assessing the quality of therapists' responses remains an open challenge.We address these challenges by first proposing a set of professional and comprehensive principles to evaluate therapists' responses to client speeches.Using these principles, we create a Psycho-Counseling Preference dataset, PsyCoPref, which contains 36k high-quality preference comparison pairs.This dataset aligns with the preferences of professional psychotherapists, providing a robust foundation for evaluating and improving LLMs in psycho-counseling.Experiments on reward modeling and preference learning demonstrate that PsyCoPref is an excellent resource for LLMs to acquire essential skills for responding to clients in a counseling session.Our best-aligned model achieves an impressive win rate of 87% against GPT-4o.We release the data and models to facilitate the research of psycho-counseling with LLMs at https://huggingface.co/Psychotherapy-LLM.
Authors: Mian Zhang, Shaun M. Eack, Zhiyu Chen
Institutions: The University of Texas at Dallas, University of Pittsburgh
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4416149546