Summary of Fast Adversarial Training Against Textual Adversarial Attacks, by Yichen Yang et al.
Fast Adversarial Training against Textual Adversarial Attacks
by Yichen Yang, Xin Liu, Kun He
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed Fast Adversarial Training (FAT) method improves the robustness of natural language processing models against adversarial attacks in a synonym-unaware scenario. Building upon observations about single-step and multi-step gradient ascent, FAT uses a single-step approach to craft adversarial examples in the embedding space, reducing the training process. Additionally, FAT leverages historical information when initializing perturbations, demonstrating its effectiveness in boosting the robustness of BERT models against various attacks with character-level and word-level modifications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FAT is a new way to make language processing models more secure. Normally, these models are not good at dealing with changes made by bad guys trying to trick them. The bad guys can change words or characters in sentences to get the model to say something it doesn’t mean. FAT helps by training the model to be better at resisting these attacks. It does this by using a special way of making tiny changes to the model’s understanding of language, called “adversarial examples”. This makes the model more robust and harder for bad guys to trick. |
Keywords
» Artificial intelligence » Bert » Boosting » Embedding space » Natural language processing
