Illuminate: A novel approach for depression detection with explainable
  analysis and proactive therapy using prompt engineering
Aryan Agrawal
This paper introduces a novel paradigm for depression detection and treatment
using advanced Large Language Models (LLMs): Generative Pre-trained Transformer
4 (GPT-4), Llama 2 chat, and Gemini. These LLMs are fine-tuned with specialized
prompts to diagnose, explain, and suggest therapeutic interventions for
depression. A unique few-shot prompting method enhances the models' ability to
analyze and explain depressive symptoms based on the DSM-5 criteria. In the
interaction phase, the models engage in empathetic dialogue management, drawing
from resources like PsychDB and a Cognitive Behavioral Therapy (CBT) Guide,
fostering supportive interactions with individuals experiencing major
depressive disorders. Additionally, the research introduces the Illuminate
Database, enriched with various CBT modules, aiding in personalized therapy
recommendations. The study evaluates LLM performance using metrics such as F1
scores, Precision, Recall, Cosine similarity, and Recall-Oriented Understudy
for Gisting Evaluation (ROUGE) across different test sets, demonstrating their
effectiveness. This comprehensive approach blends cutting-edge AI with
established psychological methods, offering new possibilities in mental health
care and showcasing the potential of LLMs in revolutionizing depression
diagnosis and treatment strategies.