An Experimental Study of Integrating Fine-tuned LLMs and Prompts for Enhancing Mental Health Support Chatbot System

Background:

Conversational mental healthcare support plays a crucial role in aiding individuals with mental health concerns. Large language models (LLMs) like GBT and BERT show potential in enhancing chat bot-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 GBT 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.

Methods:

To achieve these aims, we will review existing chat bot methodologies from rule-based systems to advanced approaches based on cognitive behavioural therapy principles (CBT). The study introduces a unique method which integrates a fine-tuned DialoGBT model along with the real-time capabilities of the ChatGBT 3.5 API. This blended combination aims to leverage the contextual awareness of LLMs and the precision of mental health-focused training. The evaluation involves a case study whereby our hybrid model is compared to traditional and standalone LLM-based chat bots. The performance is assessed using metrics such as perplexity and BLEU (Bilingual Evaluation Understudy) scores, along with subjective evaluations from end-users and mental health carers.

Results:

Our combined model outperforms others in conversational quality and relevance in mental healthcare. The positive feedback from patients and mental healthcare professionals is evidence of this. However, vital limitations highlight the need for further development in next-generation mental health support systems. Addressing these challenges is crucial for such technologies' practical application and effectiveness.

Conclusions:

With the rise of digital mental health tools, integrating models such as LLMs transforms conversational support. The study presents a promising approach combining state-of-the-art LLMs with domain-specific fine-tuned model principles. Results suggest our combined model offers affordable and better everyday support, validated by positive feedback from patients and professionals. Our research emphasises the potential of LLMs and points towards shaping responsible and effective policies for chat bot deployment in mental healthcare. These findings will contribute to future mental healthcare chat bot development and policy guidelines, emphasising the need for balanced and effective integration of advanced models and traditional therapeutic principles.