@inproceedings{huang-etal-2026-guardian,
title = "Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy {LLM}s",
author = "Huang, Yue and
Zhuang, Haomin and
Ye, Jiayi and
Bao, Han and
Wang, Yanbo and
Hua, Hang and
Wu, Siyuan and
Chen, Pin-Yu and
Zhang, Xiangliang",
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.292/",
pages = "5878--5900",
ISBN = "979-8-89176-395-1",
abstract = "Hard-gated safety checkers often over-refuse and misalign with a vendor{'}s model spec; prevailing taxonomies also neglect robustness and honesty, yielding safer-on-paper yet less useful systems. This work introduces Guardian-as-an-Advisor (GaaA), a soft-gating pipeline where a guardian predicts a binary risk label plus a concise explanation and prepends this advice to the original query for re-inference, keeping the base model operating under its original spec. To support training and evaluation, GuardSet is constructed{---}a 208k+ multi-domain dataset unifying harmful and harmless cases with targeted robustness and honesty slices. GuardAdvisor is trained via SFT followed by RL to enforce label{--}explanation consistency. GuardAdvisor attains competitive detection accuracy while enabling the advisory workflow; when used to augment inputs, responses improve over unaugmented prompts. A latency study shows advisor inference uses below 5{\%} of base-model compute and adds only 2{--}10{\%} end-to-end overhead under realistic harmful-input rates. Overall, GaaA steers models to comply with the model spec, maintaining safety while reducing over-refusal."
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<abstract>Hard-gated safety checkers often over-refuse and misalign with a vendor’s model spec; prevailing taxonomies also neglect robustness and honesty, yielding safer-on-paper yet less useful systems. This work introduces Guardian-as-an-Advisor (GaaA), a soft-gating pipeline where a guardian predicts a binary risk label plus a concise explanation and prepends this advice to the original query for re-inference, keeping the base model operating under its original spec. To support training and evaluation, GuardSet is constructed—a 208k+ multi-domain dataset unifying harmful and harmless cases with targeted robustness and honesty slices. GuardAdvisor is trained via SFT followed by RL to enforce label–explanation consistency. GuardAdvisor attains competitive detection accuracy while enabling the advisory workflow; when used to augment inputs, responses improve over unaugmented prompts. A latency study shows advisor inference uses below 5% of base-model compute and adds only 2–10% end-to-end overhead under realistic harmful-input rates. Overall, GaaA steers models to comply with the model spec, maintaining safety while reducing over-refusal.</abstract>
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%0 Conference Proceedings
%T Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs
%A Huang, Yue
%A Zhuang, Haomin
%A Ye, Jiayi
%A Bao, Han
%A Wang, Yanbo
%A Hua, Hang
%A Wu, Siyuan
%A Chen, Pin-Yu
%A Zhang, Xiangliang
%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 huang-etal-2026-guardian
%X Hard-gated safety checkers often over-refuse and misalign with a vendor’s model spec; prevailing taxonomies also neglect robustness and honesty, yielding safer-on-paper yet less useful systems. This work introduces Guardian-as-an-Advisor (GaaA), a soft-gating pipeline where a guardian predicts a binary risk label plus a concise explanation and prepends this advice to the original query for re-inference, keeping the base model operating under its original spec. To support training and evaluation, GuardSet is constructed—a 208k+ multi-domain dataset unifying harmful and harmless cases with targeted robustness and honesty slices. GuardAdvisor is trained via SFT followed by RL to enforce label–explanation consistency. GuardAdvisor attains competitive detection accuracy while enabling the advisory workflow; when used to augment inputs, responses improve over unaugmented prompts. A latency study shows advisor inference uses below 5% of base-model compute and adds only 2–10% end-to-end overhead under realistic harmful-input rates. Overall, GaaA steers models to comply with the model spec, maintaining safety while reducing over-refusal.
%U https://aclanthology.org/2026.findings-acl.292/
%P 5878-5900
Markdown (Informal)
[Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs](https://aclanthology.org/2026.findings-acl.292/) (Huang et al., Findings 2026)
ACL
- Yue Huang, Haomin Zhuang, Jiayi Ye, Han Bao, Yanbo Wang, Hang Hua, Siyuan Wu, Pin-Yu Chen, and Xiangliang Zhang. 2026. Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs. In Findings of the Association for Computational Linguistics: ACL 2026, pages 5878–5900, San Diego, California, United States. Association for Computational Linguistics.