An experimental study of integrating fine-tuned large language models and prompts for enhancing mental health support chatbot system

Abstract

Background: Conversational mental healthcare support plays a crucial role in aiding individuals with mental health concerns. Large language models (LLMs) like GPT and BERT show potential in enhancing chatbot-based therapy responses. Despite their potential, there are recognised limitations in directly deploying these LLMs for therapeutic interactions as they are trained in general context and knowledge data. The overarching aim of this study is to integrate the capabilities of both GPT and BERT with the use of specialised mental health dataset methodologies. Its goal is to enhance mental health conversations, limiting the risk and increasing quality.

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