Symptom Induction compresses labeled data into interpretable guidelines that improve LLM classification of depression symptoms in text, outperforming zero-shot, in-context, and fine-tuning approaches with gains on rare symptoms and cross-disease generalization.
Depression Detection on Social Media with Large Language Models
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The authors propose a three-layer trust framework for AI mental health systems and review current evaluation practices to highlight gaps between technical metrics and clinical requirements.
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Learning Evidence of Depression Symptoms via Prompt Induction
Symptom Induction compresses labeled data into interpretable guidelines that improve LLM classification of depression symptoms in text, outperforming zero-shot, in-context, and fine-tuning approaches with gains on rare symptoms and cross-disease generalization.
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Aligning Human-AI-Interaction Trust for Mental Health Support: Survey and Position for Multi-Stakeholders
The authors propose a three-layer trust framework for AI mental health systems and review current evaluation practices to highlight gaps between technical metrics and clinical requirements.