Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning

Dep-LLM is a novel training-free framework for Automatic Depression Detection (ADD) that leverages frozen foundation LLMs to mimic clinical reasoning. It features a three-stage process, including Chain-of-Thought (CoT) analysis for thematic decomposition, a Confidence Analysis module for reliability quantification, and a Collaborative Multi-factor Prediction for diagnosis integration. Experimental results on the DAIC-WOZ and E-DAIC datasets show that Dep-LLM outperforms both zero-shot baselines and state-of-the-art supervised models across multiple metrics, highlighting its potential for practical deployment in clinical settings without the need for extensive training.