In the past few years, it has become increasingly evident that deep neural networks are not
resilient enough to withstand adversarial perturbations in input data, leaving them …

It has been recognized that the data generated by the denoising diffusion probabilistic
model (DDPM) improves adversarial training. After two years of rapid development in …

Although backdoor learning is an active research topic in the NLP domain, the literature
lacks studies that systematically categorize and summarize backdoor attacks and defenses …

Neural ranking models (NRMs) have shown remarkable success in recent years, especially
with pre-trained language models. However, deep neural models are notorious for their …

The Differentiable Search Index (DSI) is an emerging paradigm for information retrieval.
Unlike traditional retrieval architectures where index and retrieval are two different and …

Very recently, few certified defense methods have been developed to provably guarantee
the robustness of a text classifier to adversarial synonym substitutions. However, all the …

In recent years, NLP practitioners have converged on the following practice:(i) import an off-
the-shelf pretrained (masked) language model;(ii) append a multilayer perceptron atop the …

Despite the recent success of large pretrained language models in NLP, they are
susceptible to adversarial examples. Concurrently, several studies on adversarial images …

Transformer-based pre-trained language models such as BERT have achieved remarkable
results in Semantic Sentence Matching. However, existing models still suffer from insufficient …

Adversarial attacks on deep neural networks keep raising security concerns in natural
language processing research. Existing defenses focus on improving the robustness of the …