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- W4307207905 abstract "Traditional (fickle) adversarial examples involve finding a small perturbation that does not change an input's true label but confuses the classifier into outputting a different prediction. Conversely, obstinate adversarial examples occur when an adversary finds a small perturbation that preserves the classifier's prediction but changes the true label of an input. Adversarial training and certified robust training have shown some effectiveness in improving the robustness of machine learnt models to fickle adversarial examples. We show that standard adversarial training methods focused on reducing vulnerability to fickle adversarial examples may make a model more vulnerable to obstinate adversarial examples, with experiments for both natural language inference and paraphrase identification tasks. To counter this phenomenon, we introduce Balanced Adversarial Training, which incorporates contrastive learning to increase robustness against both fickle and obstinate adversarial examples." @default.
- W4307207905 created "2022-10-30" @default.
- W4307207905 creator A5082065947 @default.
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- W4307207905 date "2022-10-20" @default.
- W4307207905 modified "2023-10-16" @default.
- W4307207905 title "Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models" @default.
- W4307207905 doi "https://doi.org/10.48550/arxiv.2210.11498" @default.
- W4307207905 hasPublicationYear "2022" @default.
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