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cs.CR2024
TaeBench: Improving Quality of Toxic Adversarial Examples
Xuan Zhu, Dmitriy Bespalov, Liwen You +2
Toxicity text detectors can be vulnerable to adversarial examples - small perturbations to input text that fool the systems into wrong detection. Existing attack algorithms are tim…
cs.CL2024
Towards Building a Robust Toxicity Predictor
Dmitriy Bespalov, Sourav Bhabesh, Yi Xiang +2
Recent NLP literature pays little attention to the robustness of toxicity language predictors, while these systems are most likely to be used in adversarial contexts. This paper pr…