Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a real challenge. Firstly, as the sentence space is discrete, it is difficult to make small …
Q Liu, R Zheng, B Rong, J Liu, Z Liu… - Proceedings of the …, 2022 - aclanthology.org
Adversarial robustness has attracted much attention recently, and the mainstream solution is adversarial training. However, the tradition of generating adversarial perturbations for each …
Large-scale pre-trained language models have achieved tremendous success across a wide range of natural language understanding (NLU) tasks, even surpassing human …
L Yuan, Y Zhang, Y Chen, W Wei - arXiv preprint arXiv:2110.15317, 2021 - arxiv.org
Despite recent success on various tasks, deep learning techniques still perform poorly on adversarial examples with small perturbations. While optimization-based methods for …
R Bao, J Wang, H Zhao - arXiv preprint arXiv:2105.14553, 2021 - arxiv.org
Pre-trained contextualized language models (PrLMs) have led to strong performance gains in downstream natural language understanding tasks. However, PrLMs can still be easily …
Y Chen, J Su, W Wei - arXiv preprint arXiv:2109.04367, 2021 - arxiv.org
Recently, the textual adversarial attack models become increasingly popular due to their successful in estimating the robustness of NLP models. However, existing works have …
Recent studies show that pre-trained language models (LMs) are vulnerable to textual adversarial attacks. However, existing attack methods either suffer from low attack success …
Adversarial examples expose the vulnerabilities of natural language processing (NLP) models, and can be used to evaluate and improve their robustness. Existing techniques of …
Despite neural networks have achieved prominent performance on many natural language processing (NLP) tasks, they are vulnerable to adversarial examples. In this paper, we …