Ren, C., Zhang, Y., He, D., & Qin, J.
(2024, June 16).
Abstract
Large language models (LLMs) are raging over the medical domain, and their momentum has carried over into the mental health domain, leading to the emergence of few mental health LLMs. Although such mental health LLMs could provide reasonable suggestions for psychological counseling, how to develop an authentic and effective doctor-patient relationship (DPR) through LLMs is still an important problem. To fill this gap, we dissect DPR into two key attributes, i.e., the psychologist's empathy and proactive guidance. We thus present WundtGPT, an empathetic and proactive mental health large language model that is acquired by fine-tuning it with instruction and real conversation between psychologists and patients. It is designed to assist psychologists in diagnosis and help patients who are reluctant to communicate face-to-face understand their psychological conditions. Its uniqueness lies in that it could not only pose purposeful questions to guide patients in detailing their symptoms but also offer warm emotional reassurance. In particular, WundtGPT incorporates Collection of Questions, Chain of Psychodiagnosis, and Empathy Constraints into a comprehensive prompt for eliciting LLMs' questions and diagnoses. Additionally, WundtGPT proposes a reward model to promote alignment with empathetic mental health professionals, which encompasses two key factors: cognitive empathy and emotional empathy. We offer a comprehensive evaluation of our proposed model. Based on these outcomes, we further conduct the manual evaluation based on proactivity, effectiveness, professionalism and coherence. We notice that WundtGPT can offer professional and effective consultation. The model is available at huggingface.
Here are some thoughts:
WundtGPT is an innovative large language model (LLM) specifically designed for mental health tasks. The model addresses three critical limitations in existing mental health LLMs: lack of goal-oriented diagnosis, insufficient proactive questioning, and ambiguous conceptualization of empathy.
The researchers developed WundtGPT by fine-tuning it using instruction and real-world conversation datasets between psychologists and patients. Its unique capabilities include posing purposeful questions to guide patients in detailing their symptoms and offering warm emotional reassurance. The model incorporates a comprehensive prompt strategy that includes a Collection of Questions, Chain of Psychodiagnosis, and Empathy Constraints.
A key innovation is the model's reward system, which promotes alignment with empathetic mental health professionals by encompassing two critical factors: cognitive empathy and emotional empathy. For cognitive empathy, the model uses an emotional detection task, while emotional empathy is aligned through reinforcement learning from human feedback.
The researchers evaluated WundtGPT from two perspectives: its ability to provide proactive diagnosis and deliver warm psychological consultation. The evaluation involved emotional benchmarking and expert assessments of the model's proactivity, effectiveness, professionalism, and coherence. Experimental results demonstrated that WundtGPT exhibits superior performance compared to baseline LLMs in simulated medical consultation scenarios.
Notably, WundtGPT is claimed to be the first proactive LLM specifically designed for mental health tasks, capable of assisting psychologists in diagnosis and helping patients who are reluctant to communicate face-to-face understand their psychological conditions.