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We propose a novel gradient-based attack against transformer-based languagemodels that searches for an adversarial example in a continuous space of tokenprobabilities. Our algorithm mitigates the gap between adversarial loss forcontinuous and discrete text representations by performing multi-stepquantization in a quantization-compensation loop. Experiments show that ourmethod significantly outperforms other approaches on various natural languageprocessing (NLP) tasks.

Step by Step Loss Goes Very Far: Multi-Step Quantization for Adversarial Text Attacks