Textual Therapies: Harnessing Language Models for Mental Health Insights

Recent years have seen a drastic rise in the occurrence of mental health issues, emphasizing the critical need for scalable approaches to identify those who are impacted. This paper introduces a novel approach that utilizes Large Language Models (LLMs), to determine whether a person is suffering from a mental illness. Specifically, it focuses on our implementation of OpenAI's Generative Pre-Trained Transformer 2 (GPT-2) model, comprising of 1.5 billion parameters. Furthermore, it has been quantized using the BaseQuantizeConfig class. We carefully examine the potential use of LLM -based technologies in the prediction of the presence of a mental illness. Our study aims to elucidate the feasibility and accuracy surrounding the deployment of LLM-based approaches in predictive analysis of mental health. We explore real-world circumstances, assessing the potential pros and cons that emerge when integrating these technologies into existing mental health screening frameworks. Through this exploration, we aim to contribute to the ongoing research on the combination of artificial intelligence and language models in mental health care, striving for a future where innovative technologies play a pivotal role in improving mental health outcomes.