@inproceedings{han-etal-2026-psyethosrag,
title = "{P}sy{E}thos{RAG}: Prompting and Retrieval for Aligning {LLM}s with Mental Health Ethics",
author = "Han, Nijia and
Zhang, Yu and
Meng, Jia and
Wang, Shuihua",
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-1.13/",
pages = "164--172",
ISBN = "979-8-89176-448-4",
abstract = "We introduce PsyEthosRAG, a frozen-model system for aligning the ethical alignment of large language models (LLMs) in mental-health contexts, combining prompting that adapts to the role of the person asking with domain-specific Retrieval-Augmented Generation (RAG) over professional ethics codes. A single frozen backbone, prompting over a task-specific backbone, answers both subtasks of the PsychEthicsEval shared task: ethical knowledge, through multiple-choice questions (MCQ) answered by an independent per-option judgement that is robust to items which do not state how many options are correct; and ethical behaviour, through open-ended questions (OEQ) answered by a prompt that adapts to the inquirer, and is grounded in retrieved guideline passages. Experiments on the Phase-1 development data, combining exact-match scoring with an LLM-judge evaluation, demonstrate the effectiveness of PsyEthosRAG in producing accurate answers on the knowledge subtask and, on the behaviour subtask, responses that decline improper requests while remaining substantively engaged rather than refusing. A cross-model comparison and a few-shot ablation further motivate our minimal, frozen configuration."
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%0 Conference Proceedings
%T PsyEthosRAG: Prompting and Retrieval for Aligning LLMs with Mental Health Ethics
%A Han, Nijia
%A Zhang, Yu
%A Meng, Jia
%A Wang, Shuihua
%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 han-etal-2026-psyethosrag
%X We introduce PsyEthosRAG, a frozen-model system for aligning the ethical alignment of large language models (LLMs) in mental-health contexts, combining prompting that adapts to the role of the person asking with domain-specific Retrieval-Augmented Generation (RAG) over professional ethics codes. A single frozen backbone, prompting over a task-specific backbone, answers both subtasks of the PsychEthicsEval shared task: ethical knowledge, through multiple-choice questions (MCQ) answered by an independent per-option judgement that is robust to items which do not state how many options are correct; and ethical behaviour, through open-ended questions (OEQ) answered by a prompt that adapts to the inquirer, and is grounded in retrieved guideline passages. Experiments on the Phase-1 development data, combining exact-match scoring with an LLM-judge evaluation, demonstrate the effectiveness of PsyEthosRAG in producing accurate answers on the knowledge subtask and, on the behaviour subtask, responses that decline improper requests while remaining substantively engaged rather than refusing. A cross-model comparison and a few-shot ablation further motivate our minimal, frozen configuration.
%U https://aclanthology.org/2026.multipsyche-1.13/
%P 164-172
Markdown (Informal)
[PsyEthosRAG: Prompting and Retrieval for Aligning LLMs with Mental Health Ethics](https://aclanthology.org/2026.multipsyche-1.13/) (Han et al., MultiPsyche 2026)
ACL