Large-scale pre-trained language models have achieved tremendous success across a
wide range of natural language understanding (NLU) tasks, even surpassing human …

Pre-trained contextualized language models (PrLMs) have led to strong performance gains
in downstream natural language understanding tasks. However, PrLMs can still be easily …

Pretrained language models (PLMs) perform poorly under adversarial attacks. To improve
the adversarial robustness, adversarial data augmentation (ADA) has been widely adopted …

Large-scale language models such as BERT have achieved state-of-the-art performance
across a wide range of NLP tasks. Recent studies, however, show that such BERT-based …

Large language models (LLMs) are now highly capable at a diverse range of tasks. This
paper studies whether or not GPT-4, one such LLM, is capable of assisting researchers in …

Despite recent success on various tasks, deep learning techniques still perform poorly on
adversarial examples with small perturbations. While optimization-based methods for …

Deep learning models have achieved great success in many fields, yet they are vulnerable
to adversarial examples. This paper follows a causal perspective to look into the adversarial …

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 …

As Large Language Models quickly become ubiquitous, their security vulnerabilities are
critical to understand. Recent work shows that text optimizers can produce jailbreaking …