Universal Adversarial Triggers

In three linesStudy on universal adversarial attacks in NLP. Authors propose a method combining POS filtering and perplexity-based loss to generate natural-sounding triggers. On SST (sentiment analysis), triggers achieve 0.04-0.12 accuracy. Adversarial training improves model robustness from 0.12 to 0.48.

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