@inproceedings{shen-etal-2026-psychethicsbench,
title = "{P}sych{E}thics{B}ench: Evaluating Large Language Models Against {A}ustralian Mental Health Ethics",
author = "Shen, Yaling and
Fong, Stephanie and
Jiang, Yiwen and
Wang, Zimu and
Tang, Feilong and
Xu, Qingyang and
Zhao, Xiangyu and
Xu, Zhongxing and
Liu, Jiahe and
Hu, Jinpeng and
Dwyer, Dominic and
Ge, Zongyuan",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1971/",
pages = "39571--39589",
ISBN = "979-8-89176-395-1",
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 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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<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 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.</abstract>
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%0 Conference Proceedings
%T PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics
%A Shen, Yaling
%A Fong, Stephanie
%A Jiang, Yiwen
%A Wang, Zimu
%A Tang, Feilong
%A Xu, Qingyang
%A Zhao, Xiangyu
%A Xu, Zhongxing
%A Liu, Jiahe
%A Hu, Jinpeng
%A Dwyer, Dominic
%A Ge, Zongyuan
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F shen-etal-2026-psychethicsbench
%X 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 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.
%U https://aclanthology.org/2026.findings-acl.1971/
%P 39571-39589
Markdown (Informal)
[PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics](https://aclanthology.org/2026.findings-acl.1971/) (Shen et al., Findings 2026)
ACL
- Yaling Shen, Stephanie Fong, Yiwen Jiang, Zimu Wang, Feilong Tang, Qingyang Xu, Xiangyu Zhao, Zhongxing Xu, Jiahe Liu, Jinpeng Hu, Dominic Dwyer, and Zongyuan Ge. 2026. PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics. In Findings of the Association for Computational Linguistics: ACL 2026, pages 39571–39589, San Diego, California, United States. Association for Computational Linguistics.