Randomized Smoothing with Masked Inference for Adversarially Robust NLP Systems

Han Cheol Moon, Shafiq Joty, Ruochen Zhao, Megh Thakkar, Xu Chi

Keywords: Natural language processing, textual adversarial example, adversarial attack, adversarial robustness

TL;DR: We propose a new framework called randomized smoothing with masked inference (RSMI) for improving adversarial robustness of NLP systems.

Abstract: Large-scale pre-trained language models have shown outstanding performance in a variety of NLP tasks. However, they are also known to be significantly brittle against specifically crafted adversarial examples, leading to increasing interest in probing the adversarial robustness of NLP systems. We introduce RSMI, a novel two-stage framework that combines randomized smoothing (RS) with masked inference (MI) to improve the adversarial robustness of NLP systems. RS transforms a classifier into a smoothed classifier to obtain robust representations, whereas MI forces a model to exploit the surrounding context of a masked token in an input sequence. RSMI improves adversarial robustness by 2 to 3 times over existing state-of-the-art methods on benchmark datasets. We also perform in-depth qualitative analysis to validate the effectiveness of the different stages of RSMI and probe the impact of its components through extensive ablations. By empirically proving the stability of RSMI, we put it forward as a practical method to robustly train large-scale NLP models. Our code and datasets are available at https://anonymous.4open.science/r/RSMI.

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