Constructing a Knowledge-Guided Mental Health Chatbot with LLMs

Xi Fan, Lishan Yang, Xiangyu Wang, Derui Lyu, Huanhuan Chen

Keywords: Large Language Model, Conversational Agent, Knowledge-Guided, Retrieval-Augmentation, Psychotherapy

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Abstract: The global shortage of mental health resources has severely impacted the ability to address psychological distress, affecting approximately 658 million people. Despite the effectiveness of psychotherapy and counseling, less than 35\% of those in need receive help. Traditional conversational agents often lack emotional support, leading to mechanical interactions that detract from user experience. This paper introduces the "Mental Health Chatbot," a conversational agent based on a pre-trained large language model. This chatbot innovatively uses retrieval-augmentation techniques to extract relevant knowledge from psychological diagnostics and treatment manuals, providing tailored psychotherapeutic interventions. It effectively identifies mental disorders and their severity, suggesting appropriate interventions. Evaluated through pre-trained model similarity comparisons, large language model scoring, and expert assessments, results show that the Mental Health Chatbot enhances the accuracy of smaller models and accelerates the inference speed of larger models through retrieval-augmentation. The optimized training process enables more human-like interactions, improving user experience and demonstrating the chatbot's potential and practical application in addressing mental health challenges.

Primary Area: Applications (bioinformatics, biomedical informatics, climate science, collaborative filtering, computer vision, healthcare, human activity recognition, information retrieval, natural language processing, social networks, etc.)

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Submission Number: 99