# Illuminate: A novel approach for depression detection with explainable analysis and proactive therapy using prompt engineering

arXiv: https://arxiv.org/abs/2402.05127 · HTML: https://mlsanity.com/paper/2402.05127/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)

## Built on by (most cited first, 5 of 5)

- [A Layered Multi-Expert Framework for Long-Context Mental Health Assessments](https://mlsanity.com/paper/2501.13951/) (2025)
- [Leveraging LLMs for Mental Health: Detection and Recommendations from Social Discussions](https://mlsanity.com/paper/2503.01442/) (2025)
- [MHINDR -- a DSM5 based mental health diagnosis and recommendation framework using LLM](https://mlsanity.com/paper/2509.25992/) (2025)
- [New AI-Driven Tools for Enhancing Campus Well-being: A Prevention and Intervention Approach](https://mlsanity.com/paper/2605.10804/) (2026)
- [From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health](https://mlsanity.com/paper/2609.25186/) (2026)

## Abstract (arXiv)

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.