Summary of Advancing the Robustness Of Large Language Models Through Self-denoised Smoothing, by Jiabao Ji et al.
Advancing the Robustness of Large Language Models through Self-Denoised Smoothing
by Jiabao Ji, Bairu Hou, Zhen Zhang, Guanhua Zhang, Wenqi Fan, Qing Li, Yang Zhang, Gaowen Liu, Sijia Liu, Shiyu Chang
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In a research effort to improve the robustness of large language models (LLMs) against adversarial attacks, researchers propose self-denoised smoothing as a more efficient and flexible approach. Unlike existing denoised smoothing techniques in computer vision that require training separate models, this method leverages the multitasking nature of LLMs to first denoise noisy inputs and then make predictions. The experimental results demonstrate that self-denoised smoothing surpasses existing methods in both empirical and certified robustness against adversarial attacks for downstream tasks and human alignments (jailbreak attacks). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be vulnerable to adversarial perturbations, including recent jailbreak attacks. To improve their robustness without requiring full access to the model’s parameters or fine-tuning via adversarial training, researchers propose self-denoised smoothing. This method uses the multitasking nature of LLMs to denoise noisy inputs and then make predictions. Self-denoised smoothing is a more efficient and flexible approach compared to existing denoised smoothing techniques. |
Keywords
» Artificial intelligence » Fine tuning
