Abstract
Recent advancements in Large Language Models (LLMs) have led to their widespread adoption in daily applications. Despite their impressive capabilities, they remain vulnerable to adversarial attacks, as even minor meaning-preserving changes such as synonym substitutions can lead to incorrect predictions. Therefore, certifying the robustness of LLMs against such adversarial prompts is of vital importance.
Existing approaches have focused on word deletion or simple denoising strategies to achieve robustness certification. However, these methods face two critical limitations:
- They yield loose robustness bounds due to a lack of semantic validation for perturbed outputs.
- They suffer from high computational costs due to repeated sampling.
To address these limitations, we propose CluCERT, a novel framework for certifying LLM robustness via clustering-guided denoising smoothing. Specifically, to achieve tighter certified bounds, we introduce a semantic clustering filter that reduces noisy samples while retaining meaningful perturbations, supported by theoretical analysis.
Furthermore, we enhance computational efficiency through two mechanisms:
- A refine module that extracts core semantics.
- A fast synonym substitution strategy that accelerates the denoising process.
Finally, we conduct extensive experiments on various downstream tasks and jailbreak defense scenarios. Experimental results demonstrate that our method outperforms existing certified approaches in both robustness bounds and computational efficiency.
Blogger's Review: The introduction of CluCERT offers a fresh perspective on certifying the robustness of large language models. By effectively addressing the limitations of current methods through clustering-guided denoising, this approach not only enhances theoretical foundations but also shows promising practical applications, making it a significant contribution to the field of adversarial defense.