Improving the Adversarial Robustness of NLP Models by Information Bottleneck

Cho-Jui Hsieh, Kai-Wei Chang, Xiang Zhou, Xiaoqing Zheng, Cenyuan Zhang, Yixin Wan

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Abstract

Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manipulated by adversaries to fool NLP models. In this study, we explore the feasibility of capturing task-specific robust features, while eliminating the non-robust ones by using the information bottleneck theory. Through extensive experiments, we show that the models trained with our information bottleneck-based method are able to achieve a significant improvement in robust accuracy, exceeding performances of all the previously reported defense methods while suffering almost no performance drop in clean accuracy on SST-2, AGNEWS and IMDB datasets.

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