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    <abstract>In light of the widespread deployment of Large Language Models (LLMs), the responsibility for safeguarding and regulating LLM-generated content has taken on heightened significance. Recent advancements in LLM-based moderation methods, e.g., LlamaGuard, have demonstrated remarkable promise in identifying safety risks associated with both inputs and outputs in human-AI interactions. However, integrating LLM-based safeguards into a chatbot system requires an additional inference stage involving a moderation LLM with billions of parameters, which significantly increases computational costs and reduces overall efficiency. In this paper, we demonstrate that simply learning a classification head on the last-layer hidden states of the dialogue model provides a strong capability to identify harmful contents. The classification head, referred to as ShieldHead, serves as an auxiliary branch paralleled with next-token-prediction LM head, enabling the detection of potential risks in past text sequences. Additionally, a label disambiguation technique is employed to supervise ShieldHead with both token-level and sentence-level labels, which further enhances its performance. ShieldHead exhibits remarkable efficiency during inference, providing real-time moderation results alongside token-wise streaming output during the chatbot system’s decoding phase. Extensive experimental results demonstrate the superiority of the proposed framework: a state-of-the-art performance on the XSTest and SafeRLHF datasets while running at a speed about **300×** faster (**\ensuremath&lt;1ms**) than previous LLM-based moderation models with ** 99%** less parameters of LlamaGuard.</abstract>
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%0 Conference Proceedings
%T ShieldHead: Decoding-time Safeguard for Large Language Models
%A Xuan, Zitao
%A Mao, Xiaofeng
%A Chen, Da
%A Zhang, Xin
%A Dong, Yuhan
%A Zhou, Jun
%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 xuan-etal-2025-shieldhead
%X In light of the widespread deployment of Large Language Models (LLMs), the responsibility for safeguarding and regulating LLM-generated content has taken on heightened significance. Recent advancements in LLM-based moderation methods, e.g., LlamaGuard, have demonstrated remarkable promise in identifying safety risks associated with both inputs and outputs in human-AI interactions. However, integrating LLM-based safeguards into a chatbot system requires an additional inference stage involving a moderation LLM with billions of parameters, which significantly increases computational costs and reduces overall efficiency. In this paper, we demonstrate that simply learning a classification head on the last-layer hidden states of the dialogue model provides a strong capability to identify harmful contents. The classification head, referred to as ShieldHead, serves as an auxiliary branch paralleled with next-token-prediction LM head, enabling the detection of potential risks in past text sequences. Additionally, a label disambiguation technique is employed to supervise ShieldHead with both token-level and sentence-level labels, which further enhances its performance. ShieldHead exhibits remarkable efficiency during inference, providing real-time moderation results alongside token-wise streaming output during the chatbot system’s decoding phase. Extensive experimental results demonstrate the superiority of the proposed framework: a state-of-the-art performance on the XSTest and SafeRLHF datasets while running at a speed about **300×** faster (**\ensuremath<1ms**) than previous LLM-based moderation models with ** 99%** less parameters of LlamaGuard.
%R 10.18653/v1/2025.findings-acl.932
%U https://aclanthology.org/2025.findings-acl.932/
%U https://doi.org/10.18653/v1/2025.findings-acl.932
%P 18129-18143

Markdown (Informal)

[ShieldHead: Decoding-time Safeguard for Large Language Models](https://aclanthology.org/2025.findings-acl.932/) (Xuan et al., Findings 2025)

• ShieldHead: Decoding-time Safeguard for Large Language Models (Xuan et al., Findings 2025)

ACL

• Zitao Xuan, Xiaofeng Mao, Da Chen, Xin Zhang, Yuhan Dong, and Jun Zhou. 2025. ShieldHead: Decoding-time Safeguard for Large Language Models. In Findings of the Association for Computational Linguistics: ACL 2025, pages 18129–18143, Vienna, Austria. Association for Computational Linguistics.