← 研踪 PaperTrailarXiv:2602.00950 · v2
MindGuard: Guardrail Classifiers for Multi-Turn Mental Health Support
首次提交 2026-02-01 09:03
版本更新 2026-11-03 06:21
雷达观测 2026-11-04 23:40
摘要
Large language models are increasingly used for mental health support, yet their conversational coherence alone does not ensure clinical appropriateness. Existing general-purpose safeguards often fail to distinguish therapeutic disclosures from genuine clinical crises, and progress on developing better ones is bottlenecked by a lack of clinically grounded evaluation resources. To address this gap, we introduce a risk taxonomy, developed in collaboration with PhD-level clinical psychologists, that identifies actionable harm (self-harm and harm to others) while preserving space for safe, non-crisis therapeutic content. We release MindGuard-testset, a dataset of multi-turn conversations annotated at the turn level by clinical experts, which is, to our knowledge, the first public benchmark with turn-level clinical risk labels for multi-turn mental health support. We also release an automated red-teaming (ART) framework designed to measure how safety classifiers affect downstream model behavior in adversarial multi-turn interactions. Using synthetic dialogues generated via a controlled two-agent setup, we train MindGuard, a family of lightweight safety classifiers (with 4B and 8B parameters). Our classifiers reduce false positives at high-recall operating points and, when paired with clinician language models, help achieve lower attack success and harmful engagement rates compared to general-purpose safeguards. We release all models, training and human evaluation data, and the ART framework.
作者备注：NeurIPS 2026, Track on Evaluations and Datasets
观测到的版本
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v2 · 2026-11-03 06:21 · 最新观测版本
MindGuard: Guardrail Classifiers for Multi-Turn Mental Health Support
Large language models are increasingly used for mental health support, yet their conversational coherence alone does not ensure clinical appropriateness. Existing general-purpose safeguards often fail to distinguish therapeutic disclosures from genuine clinical crises, and progress on developing better ones is bottlenecked by a lack of clinically grounded evaluation resources. To address this gap, we introduce a risk taxonomy, developed in collaboration with PhD-level clinical psychologists, that identifies actionable harm (self-harm and harm to others) while preserving space for safe, non-crisis therapeutic content. We release MindGuard-testset, a dataset of multi-turn conversations annotated at the turn level by clinical experts, which is, to our knowledge, the first public benchmark with turn-level clinical risk labels for multi-turn mental health support. We also release an automated red-teaming (ART) framework designed to measure how safety classifiers affect downstream model behavior in adversarial multi-turn interactions. Using synthetic dialogues generated via a controlled two-agent setup, we train MindGuard, a family of lightweight safety classifiers (with 4B and 8B parameters). Our classifiers reduce false positives at high-recall operating points and, when paired with clinician language models, help achieve lower attack success and harmful engagement rates compared to general-purpose safeguards. We release all models, training and human evaluation data, and the ART framework.
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