Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning
Automatic Depression Detection (ADD) from clinical interviews is a pivotal task in computational mental health, yet it remains challenging due to two critical obstacles: 1) difficulty in modeling complex but sparsely distributed depression clues within lengthy, multi-topic clinical interviews, leading to superficial and unreliable reasoning; 2) scarcity of labeled data due to clinical privacy, together with high cost of training and fine-tuning, limiting the deployment of supervised ADD systems.
Record details
Published: 9 June 2026
Source: arXiv
Category: Research
Topics: Privacy · Healthcare
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (9 June 2026), “Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning,” evidence record 1199, https://ethics.ai/record/1199 (originally published by arXiv).
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