A deliberative council of Gemini agents using absence-based clinical rules achieves 0.382 F1 without fine-tuning and second place overall at 0.406 F1 on defense mechanism classification, with minority-class overrides adding 2.4pp.
Findings of the Association for Computational Linguistics: ACL 2025 , month=jul, year=
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A multi-axis 9-voter ensemble for psychological defence mechanism classification wins the PsyDefDetect shared task with F1=0.420.
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.
A context-aware synthetic augmentation framework with a hybrid clinical-language model improves psychological defense mechanism classification to 58.26% accuracy and 24.62% macro-F1 in low-resource conditions, outperforming the DMRS Co-Pilot baseline.
citing papers explorer
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UTS at PsyDefDetect: Multi-Agent Councils and Absence-Based Reasoning for Defense Mechanism Classification
A deliberative council of Gemini agents using absence-based clinical rules achieves 0.382 F1 without fine-tuning and second place overall at 0.406 F1 on defense mechanism classification, with minority-class overrides adding 2.4pp.
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N\"urnberg NLP at PsyDefDetect: Multi-Axis Voter Ensembles for Psychological Defence Mechanism Classification
A multi-axis 9-voter ensemble for psychological defence mechanism classification wins the PsyDefDetect shared task with F1=0.420.
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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.
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Mitigating Data Scarcity in Psychological Defense Classification with Context-Aware Synthetic Augmentation
A context-aware synthetic augmentation framework with a hybrid clinical-language model improves psychological defense mechanism classification to 58.26% accuracy and 24.62% macro-F1 in low-resource conditions, outperforming the DMRS Co-Pilot baseline.