Dep-LLM is a training-free three-stage LLM framework that decomposes clinical interviews into clinical themes, modulates signals by token entropy, and outperforms zero-shot and supervised baselines on DAIC-WOZ and E-DAIC datasets.
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Audit of depression detection benchmarks finds that official splits yield unstable model rankings, zero-shot transfer across datasets is weak, and text models but not audio models improve on symptom-dense interview segments.
citing papers explorer
-
Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning
Dep-LLM is a training-free three-stage LLM framework that decomposes clinical interviews into clinical themes, modulates signals by token entropy, and outperforms zero-shot and supervised baselines on DAIC-WOZ and E-DAIC datasets.
-
A Multi-Probe Audit of Clinical-Interview Depression Detection Benchmarks
Audit of depression detection benchmarks finds that official splits yield unstable model rankings, zero-shot transfer across datasets is weak, and text models but not audio models improve on symptom-dense interview segments.