WundtGPT: Shaping Large Language Models To Be An Empathetic, Proactive Psychologist
<|reference_start|>WundtGPT: Shaping Large Language Models To Be An Empathetic, Proactive Psychologist: 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.<|reference_end|>
@article{ren2024wundtgpt:,
    title={WundtGPT: Shaping Large Language Models To Be An Empathetic, Proactive
  Psychologist},
    author={Chenyu Ren, Yazhou Zhang, Daihai He, Jing Qin},
    journal={arXiv preprint arXiv:2406.15474},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.15474},
    primaryClass={cs.AI cs.CL cs.HC}
}