PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics

• URL: http://arxiv.org/abs/2601.03578v1
• Date: Wed, 07 Jan 2026 04:49:02 GMT
• Title: PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics
• Authors: Yaling Shen, Stephanie Fong, Yiwen Jiang, Zimu Wang, Feilong Tang, Qingyang Xu, Xiangyu Zhao, Zhongxing Xu, Jiahe Liu, Jinpeng Hu, Dominic Dwyer, Zongyuan Ge,
• Abstract summary: In mental health, clinically inadequate refusals can be perceived as unempathetic and discourage help-seeking.<n>To address this gap, we move beyond refusal-centric metrics and introduce textttPsychEthicsBench, the first principle-grounded benchmark based on Australian psychology and psychiatry guidelines.<n> Empirical results across 14 models reveal that refusal rates are poor indicators of ethical behavior, revealing a significant divergence between safety triggers and clinical appropriateness.
• Score: 35.52940216380734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The increasing integration of large language models (LLMs) into mental health applications necessitates robust frameworks for evaluating professional safety alignment. Current evaluative approaches primarily rely on refusal-based safety signals, which offer limited insight into the nuanced behaviors required in clinical practice. In mental health, clinically inadequate refusals can be perceived as unempathetic and discourage help-seeking. To address this gap, we move beyond refusal-centric metrics and introduce \texttt{PsychEthicsBench}, the first principle-grounded benchmark based on Australian psychology and psychiatry guidelines, designed to evaluate LLMs' ethical knowledge and behavioral responses through multiple-choice and open-ended tasks with fine-grained ethicality annotations. Empirical results across 14 models reveal that refusal rates are poor indicators of ethical behavior, revealing a significant divergence between safety triggers and clinical appropriateness. Notably, we find that domain-specific fine-tuning can degrade ethical robustness, as several specialized models underperform their base backbones in ethical alignment. PsychEthicsBench provides a foundation for systematic, jurisdiction-aware evaluation of LLMs in mental health, encouraging more responsible development in this domain.

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