MENTAL HEALTH AND STRESS PREDICTION USING LARGE LANGUAGE MODELS

Abstract

In recent times, the evolution of Large Language Models (LLMs) has brought about transformative breakthroughs in many real-life applications including mental health. The continuous advancement of artificial intelligence and natural language processing techniques has led to noteworthy achievements, with LLMs showcasing considerable potential in the detection and prediction of various mental health issues. We present insights into the role of Large Language Models in mental health detection, especially focusing on anxiety, depression, and stress detection. We first present a taxonomy for the categorization of current research based on several methods used, including prompt engineering, fine-tuning, and instruction fine-tuning. The core of this research focuses on the methodologies employed in recent studies where LLMs have been utilized for detecting mental health and analyzing the performance of various models on tasks like anxiety detection, depression detection, and stress detection. This study includes an analysis of different models, datasets, and algorithmic approaches, along with the integration of LLMs into healthcare systems, focusing on examining the strengths and limitations of different techniques highlighting the challenges, opportunities, and future gaps in mental health using large language models. A range of performance metrics including accuracy, precision, recall, and F1-score have been employed to assess the overall efficacy of the models.