Minds and Machines: Revolutionizing Mental Health Care Through AI

Artificial Intelligence has immense potential in mental health care in the last few years, satisfying the increasing demand for more accessible and effective solutions. This research paper investigates the performance of Large Language Models like RoBERTa, Emotion-BERT, and Mental-BERT fine-tuned for mental health applications using advanced techniques: Low-Rank Adaptation, QLoRA quantization, and Odds Ratio Preference Optimization. In this research, we assess the effect of these fine-tuning methods on important metrics such as accuracy, precision, recall, and F1 score using a detailed dataset that focuses on mental health counseling. The study highlights the advantages of these cutting-edge fine-tuning techniques, not just for improving LLM performance, but for making them more interpretable and adaptable for therapeutic use. In particular, LoRA is effective for reducing computational costs, QLoRA can retain model accuracy with high compression, and ORPO is effective for aligning model outputs with user preferences. After some considerable testing with various articles, it was concluded that ORPO has the best balance in AT for both accuracy and user satisfaction. This study highlights the revolutionary power of AI-powered approaches in transforming mental health treatment into scalable, tailored interventions. It provides important information about the adaptation of LLMs for therapeutic purposes, laying the groundwork for their use in the clinic and in mental health applications.