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thenewstack.io·16h ago
AI Research
arXiv CS.AI·Jun 10·5 min read
Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning
TL;DR
Automatic Depression Detection (ADD) faces challenges due to complex clues in lengthy clinical interviews and limited labeled data. Dep-LLM is a training-free framework that uses off-the-shelf large language models (LLMs) to analyze dialogues and produce reliable diagnoses.
✦ Why It Matters
Engineers can implement Dep-LLM for efficient and reliable depression diagnosis without extensive training data.
Key Takeaways
- Utilize Dep-LLM for training-free depression diagnosis.
- Implement Chain-of-Thought analysis for complex dialogue.
- Leverage confidence modulation to enhance diagnosis reliability.
- Test on DAIC-WOZ and E-DAIC datasets for validation.
How It Works
Dep-LLM operates in three stages: first, it decomposes clinical dialogues into five themes using a Chain-of-Thought approach, allowing for structured analysis. Next, it evaluates the reliability of each theme's rationale through a Confidence Analysis module, which uses token-level entropy to filter out uncertain signals.
Finally, it integrates these insights into a final diagnosis through a Collaborative Multi-factor Prediction module, ensuring that the most reliable information is weighted more heavily.