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    <abstract>Ensuring the safety of large language models (LLMs) is critical as they are deployed in real-world applications. Existing guardrails rely on rule-based filtering or single-pass classification, limiting their ability to handle nuanced safety violations. To address this, we propose ThinkGuard, a critique-augmented guardrail model that distills knowledge from high-capacity LLMs by generating structured critiques alongside safety labels. Fine-tuned on critique-augmented data, the captured deliberative thinking ability drastically enhances the guardrail’s cautiousness and interpretability. Evaluated on multiple safety benchmarks, ThinkGuard achieves the highest average F1 and AUPRC, outperforming all baselines. Compared to LLaMA Guard 3, ThinkGuard improves accuracy by 16.1% and macro F1 by 27.0%. Moreover, it surpasses label-only fine-tuned models, confirming that structured critiques enhance both classification precision and nuanced safety reasoning while maintaining computational efficiency.</abstract>
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%0 Conference Proceedings
%T ThinkGuard: Deliberative Slow Thinking Leads to Cautious Guardrails
%A Wen, Xiaofei
%A Zhou, Wenxuan
%A Mo, Wenjie Jacky
%A Chen, Muhao
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F wen-etal-2025-thinkguard
%X Ensuring the safety of large language models (LLMs) is critical as they are deployed in real-world applications. Existing guardrails rely on rule-based filtering or single-pass classification, limiting their ability to handle nuanced safety violations. To address this, we propose ThinkGuard, a critique-augmented guardrail model that distills knowledge from high-capacity LLMs by generating structured critiques alongside safety labels. Fine-tuned on critique-augmented data, the captured deliberative thinking ability drastically enhances the guardrail’s cautiousness and interpretability. Evaluated on multiple safety benchmarks, ThinkGuard achieves the highest average F1 and AUPRC, outperforming all baselines. Compared to LLaMA Guard 3, ThinkGuard improves accuracy by 16.1% and macro F1 by 27.0%. Moreover, it surpasses label-only fine-tuned models, confirming that structured critiques enhance both classification precision and nuanced safety reasoning while maintaining computational efficiency.
%R 10.18653/v1/2025.findings-acl.704
%U https://aclanthology.org/2025.findings-acl.704/
%U https://doi.org/10.18653/v1/2025.findings-acl.704
%P 13698-13713

Markdown (Informal)

[ThinkGuard: Deliberative Slow Thinking Leads to Cautious Guardrails](https://aclanthology.org/2025.findings-acl.704/) (Wen et al., Findings 2025)

• ThinkGuard: Deliberative Slow Thinking Leads to Cautious Guardrails (Wen et al., Findings 2025)

ACL

• Xiaofei Wen, Wenxuan Zhou, Wenjie Jacky Mo, and Muhao Chen. 2025. ThinkGuard: Deliberative Slow Thinking Leads to Cautious Guardrails. In Findings of the Association for Computational Linguistics: ACL 2025, pages 13698–13713, Vienna, Austria. Association for Computational Linguistics.