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.
Enhancing depression detection with chain-of-thought prompting: From emotion to reasoning using large language models,
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A survey proposing a three-pillar framework to evaluate LLMs as tools for measuring latent psychological constructs and reviewing applications in personality and mental health.
A systematic review of AI for depressive disorder detection that introduces a novel hierarchical taxonomy organized by clinical task, data modality, and model class.
A survey of reasoning language model adoption across 28 ERC scientific disciplines finds large maturity gaps, especially when only public resources are counted.
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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.
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A Survey of Large Language Models for Perception and Measurement of Human Psychology
A survey proposing a three-pillar framework to evaluate LLMs as tools for measuring latent psychological constructs and reviewing applications in personality and mental health.
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AI Models for Depressive Disorder Detection and Diagnosis: A Review
A systematic review of AI for depressive disorder detection that introduces a novel hierarchical taxonomy organized by clinical task, data modality, and model class.
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Reasoning4Sciences: Bridging Reasoning Language Models to All Scientific Branches
A survey of reasoning language model adoption across 28 ERC scientific disciplines finds large maturity gaps, especially when only public resources are counted.