Advancing the Robustness of Large Language Models through Self-Denoised Smoothing

arXiv:2404.12274v1 Announce Type: new
Abstract: Although large language models (LLMs) have achieved significant success, their vulnerability to adversarial perturbations, including recent jailbreak attacks, has raised considerable concerns. However, the increasing size of these models and their limited access make improving their robustness a challenging task. Among various defense strategies, randomized smoothing has shown great potential for LLMs, as it does not require full access to the model's parameters or fine-tuning via adversarial training. However, randomized smoothing involves adding noise to …

arxiv cs.ai cs.cl language language models large language large language models robustness through type