Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions

• URL: http://arxiv.org/abs/2503.01442v1
• Date: Mon, 03 Mar 2025 11:48:01 GMT
• Title: Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions
• Authors: Vaishali Aggarwal, Sachin Thukral, Krushil Patel, Arnab Chatterjee,
• Abstract summary: We propose a comprehensive framework that leverages Natural Language Processing (NLP) and Generative AI techniques to identify and assess mental health disorders.<n>We use rule-based labeling methods as well as advanced pre-trained NLP models to extract nuanced semantic features from the data.<n>We fine-tune domain-adapted and generic pre-trained NLP models based on predictions from specialized Large Language Models (LLMs) to improve classification accuracy.
• Score: 0.5249805590164902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Textual data from social platforms captures various aspects of mental health through discussions around and across issues, while users reach out for help and others sympathize and offer support. We propose a comprehensive framework that leverages Natural Language Processing (NLP) and Generative AI techniques to identify and assess mental health disorders, detect their severity, and create recommendations for behavior change and therapeutic interventions based on users' posts on Reddit. To classify the disorders, we use rule-based labeling methods as well as advanced pre-trained NLP models to extract nuanced semantic features from the data. We fine-tune domain-adapted and generic pre-trained NLP models based on predictions from specialized Large Language Models (LLMs) to improve classification accuracy. Our hybrid approach combines the generalization capabilities of pre-trained models with the domain-specific insights captured by LLMs, providing an improved understanding of mental health discourse. Our findings highlight the strengths and limitations of each model, offering valuable insights into their practical applicability. This research potentially facilitates early detection and personalized care to aid practitioners and aims to facilitate timely interventions and improve overall well-being, thereby contributing to the broader field of mental health surveillance and digital health analytics.

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