Summary of Towards Building a Robust Toxicity Predictor, by Dmitriy Bespalov et al.
Towards Building a Robust Toxicity Predictor
by Dmitriy Bespalov, Sourav Bhabesh, Yi Xiang, Liutong Zhou, Yanjun Qi
First submitted to arxiv on: 9 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); 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 proposes a novel adversarial attack called ToxicTrap to evaluate the robustness of state-of-the-art (SOTA) text classifiers in predicting toxic language. The attack introduces small perturbations at the word level, fooling SOTA text classifiers into misclassifying toxic samples as benign. ToxicTrap utilizes greedy search strategies to efficiently generate adversarial examples and identifies weaknesses in both multiclass and multilabel toxicity detectors using novel goal function designs. Empirical results demonstrate that SOTA toxicity text classifiers are vulnerable to these attacks, achieving over 98% attack success rates in multilabel cases. Furthermore, the paper explores how vanilla adversarial training and its improved version can improve the robustness of a toxicity detector against unseen attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure language prediction systems don’t get tricked into thinking mean things are okay when they’re not. Right now, most language prediction systems aren’t very good at dealing with tricky situations where people try to manipulate them. The researchers created a new way to test these systems by introducing small changes in words that make the system think something is fine when it’s actually toxic. They showed that many of the best language prediction systems can be tricked into making mistakes this way, and they also found ways to make some of these systems more robust against these attacks. |
