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 …

Recent natural language processing (NLP) techniques have accomplished high
performance on benchmark data sets, primarily due to the significant improvement in the …

The prompt-based learning paradigm, which bridges the gap between pre-training and fine-
tuning, achieves state-of-the-art performance on several NLP tasks, particularly in few-shot …

Recently, Large Language Models (LLMs) have made significant advancements and are
now widely used across various domains. Unfortunately, there has been a rising concern …

Adversarial training, a method for learning robust deep neural networks, constructs
adversarial examples during training. However, recent methods for generating NLP …

High-performance neural language models have obtained state-of-the-art results on a wide
range of Natural Language Processing (NLP) tasks. However, results for common …

Recent studies have shown that deep neural networks are vulnerable to intentionally crafted
adversarial examples, and various methods have been proposed to defend against …

The fine-tuning of pre-trained language models has a great success in many NLP fields. Yet,
it is strikingly vulnerable to adversarial examples, eg, word substitution attacks using only …

The question answering system is frequently applied in the area of natural language
processing (NLP) because of the wide variety of applications. It consists of answering …

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 …