MHSafeEval applies a new role-aware taxonomy to discover cumulative mental health harms in LLM counseling trajectories via adversarial multi-turn interactions, revealing failures missed by static benchmarks.
In2025 IEEE Conference on Se- cure and Trustworthy Machine Learning (SaTML), pages 23–42
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MHSafeEval: Role-Aware Interaction-Level Evaluation of Mental Health Safety in Large Language Models
MHSafeEval applies a new role-aware taxonomy to discover cumulative mental health harms in LLM counseling trajectories via adversarial multi-turn interactions, revealing failures missed by static benchmarks.