Adversarial attacks pose a significant threat to the reliability of pre-trained language models
(PLMs) such as GPT, BERT, RoBERTa, and T5. This paper presents Adversarial …

This paper proposes a simple yet effective jailbreak attack named FlipAttack against black-
box LLMs. First, from the autoregressive nature, we reveal that LLMs tend to understand the …

Interestingly, LLMs yet struggle with some basic tasks that humans find trivial to handle, eg,
counting the number of character r's in the word" strawberry". There are several popular …

Generative Adversarial Networks (GANs) have emerged as a powerful type of generative
model, particularly effective at creating images from textual descriptions. Similar to diffusion …

Financial sentiment analysis models, which extract meaning from vast amounts of
unstructured data, play a crucial role in sentiment-driven financial decisions. However, the …

Text classifiers suffer from small perturbations, that if chosen adversarially, can dramatically
change the output of the model. Verification methods can provide robustness certificates …

Adversarial attacks on financial sentiment analysis models are a critical area of research
within NLP. We introduce a novel white-box attack method that leverages a pre-trained …

• Enhancing Robustness of Pre-trained Language Models: The primary objective of this work
is to improve the robustness of pre-trained language models like BERT, ALBERT, and …

Natural Language Processing (NLP) models suffer from small perturbations, that if chosen
adversarially, can dramatically change the output of the model. Verification methods can …