@inproceedings{zhou-etal-2026-training,
title = "Training-Free Ethics-Aware Prompting and Safety- and Identity-Aware Retrieval-Augmented Generation",
author = "Zhou, Junjie and
Wang, Junran and
Tang, Ruize",
editor = "Wang, Zimu and
Jiang, Yiwen and
Shen, Yaling and
Zhao, Xiangyu and
Fong, Stephanie and
Sun, Xin and
Zhou, Yi and
Ji, Shaoxiong and
Desimone, Robert and
Nelson, Barnaby and
Dwyer, Dominic and
Ge, Zongyuan",
booktitle = "Proceedings of the 1st Workshop on Multimodal, Multilingual, and Multicultural Mental Health and Psychotherapy ({M}ulti{P}syche)",
month = nov,
year = "2026",
address = "Hengqin, Zhuhai, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.multipsyche-1.14/",
pages = "173--180",
ISBN = "979-8-89176-448-4",
abstract = "Large language models are increasingly used in mental health applications, where responses must satisfy not only general safety requirements but also context-dependent ethical, professional, and jurisdictional constraints. We present a training-free inference framework for the MultiPsyche-2026 PsychEthicsEval shared task. For multiple-choice questions (Task I), the system uses ethics-aware structured prompting with independent option assessment and constrained machine-readable outputs. For open-ended questions (Task II), we introduce an identity-grounded and permission-bounded retrieval-augmented generation pipeline over curated Australian mental-health ethics resources. On the official hidden MCQ test set consisting of 282 samples, our system achieved a Partial Credit score of 0.768 (95{\%} CI: 0.718{--}0.814) and an Exact Match score of 0.762. On 2,512 OEQ responses, the system obtained an ethical pass rate of 0.796 under the ``rule pre-screening + classifier'' evaluation and 0.621 under the independent LLM-as-a-Judge evaluation. The difference between the two OEQ evaluation scores indicates that the measurement results of open-ended ethical responses are influenced by how ethical violations are operationalized and evaluated."
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<abstract>Large language models are increasingly used in mental health applications, where responses must satisfy not only general safety requirements but also context-dependent ethical, professional, and jurisdictional constraints. We present a training-free inference framework for the MultiPsyche-2026 PsychEthicsEval shared task. For multiple-choice questions (Task I), the system uses ethics-aware structured prompting with independent option assessment and constrained machine-readable outputs. For open-ended questions (Task II), we introduce an identity-grounded and permission-bounded retrieval-augmented generation pipeline over curated Australian mental-health ethics resources. On the official hidden MCQ test set consisting of 282 samples, our system achieved a Partial Credit score of 0.768 (95% CI: 0.718–0.814) and an Exact Match score of 0.762. On 2,512 OEQ responses, the system obtained an ethical pass rate of 0.796 under the “rule pre-screening + classifier” evaluation and 0.621 under the independent LLM-as-a-Judge evaluation. The difference between the two OEQ evaluation scores indicates that the measurement results of open-ended ethical responses are influenced by how ethical violations are operationalized and evaluated.</abstract>
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%0 Conference Proceedings
%T Training-Free Ethics-Aware Prompting and Safety- and Identity-Aware Retrieval-Augmented Generation
%A Zhou, Junjie
%A Wang, Junran
%A Tang, Ruize
%Y Wang, Zimu
%Y Jiang, Yiwen
%Y Shen, Yaling
%Y Zhao, Xiangyu
%Y Fong, Stephanie
%Y Sun, Xin
%Y Zhou, Yi
%Y Ji, Shaoxiong
%Y Desimone, Robert
%Y Nelson, Barnaby
%Y Dwyer, Dominic
%Y Ge, Zongyuan
%S Proceedings of the 1st Workshop on Multimodal, Multilingual, and Multicultural Mental Health and Psychotherapy (MultiPsyche)
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, Zhuhai, China
%@ 979-8-89176-448-4
%F zhou-etal-2026-training
%X Large language models are increasingly used in mental health applications, where responses must satisfy not only general safety requirements but also context-dependent ethical, professional, and jurisdictional constraints. We present a training-free inference framework for the MultiPsyche-2026 PsychEthicsEval shared task. For multiple-choice questions (Task I), the system uses ethics-aware structured prompting with independent option assessment and constrained machine-readable outputs. For open-ended questions (Task II), we introduce an identity-grounded and permission-bounded retrieval-augmented generation pipeline over curated Australian mental-health ethics resources. On the official hidden MCQ test set consisting of 282 samples, our system achieved a Partial Credit score of 0.768 (95% CI: 0.718–0.814) and an Exact Match score of 0.762. On 2,512 OEQ responses, the system obtained an ethical pass rate of 0.796 under the “rule pre-screening + classifier” evaluation and 0.621 under the independent LLM-as-a-Judge evaluation. The difference between the two OEQ evaluation scores indicates that the measurement results of open-ended ethical responses are influenced by how ethical violations are operationalized and evaluated.
%U https://aclanthology.org/2026.multipsyche-1.14/
%P 173-180
Markdown (Informal)
[Training-Free Ethics-Aware Prompting and Safety- and Identity-Aware Retrieval-Augmented Generation](https://aclanthology.org/2026.multipsyche-1.14/) (Zhou et al., MultiPsyche 2026)
ACL