Summary of Certified Robustness For Large Language Models with Self-denoising, by Zhen Zhang et al.
Certified Robustness for Large Language Models with Self-Denoising
by Zhen Zhang, Guanhua Zhang, Bairu Hou, Wenqi Fan, Qing Li, Sijia Liu, Yang Zhang, Shiyu Chang
First submitted to arxiv on: 14 Jul 2023
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the limitations of large language models (LLMs) in high-stake environments, where small variations in input data can significantly impact predictions. Researchers have made progress in certifying the robustness of LLMs using randomized smoothing, but this approach requires adding noise to inputs and depends on the model’s performance on corrupted data. To overcome these limitations, the authors propose a self-denoising method that leverages the multitasking nature of LLMs to denoise corrupted inputs in a more efficient and flexible manner. The proposed method outperforms existing certification methods under both certified robustness and empirical robustness, as demonstrated by experiment results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure computer models are reliable when they’re used in important situations. Right now, big language models can be tricked into giving different answers if the input data is slightly changed. The authors want to fix this problem by finding a way to make the models more stable. They tried an approach called randomized smoothing, but it didn’t work well because it requires adding fake noise to the data and relies on how well the model performs when it’s dealing with noisy data. To solve this issue, they came up with a new method that uses the model itself to clean up the noisy data. This new method works better than previous methods in both theoretical and real-world tests. |
