Addressing the Challenges of Mental Health Conversations with Large Language Models

Virtual Mental Health Assistants offer a promising solution to address the growing demand for accessible and scalable mental healthcare. However, existing dialogue generation models struggle with the complexities inherent in mental health conversations. In this paper, we explore the limitations of current Medical Dialogue Generation models by conducting experiments on the large language model ChatMGL. We propose modifications to ChatMGL, including fine-tuning the model on a mental health dataset without proximal policy optimization and incorporating dialogue act labels, to enhance its ability to handle the complex nature of mental health dialogues. Our results demonstrate that these modifications outperform baseline models in terms of ROUGE and BERT scores. Our work suggests that specialized fine-tuning and incorporating domain-specific knowledge can improve the efficacy of virtual assistants for mental health support.