Shirin Nilizadeh

Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions

Large Language Models (LLMs) have introduced novel opportunities for text comprehension and generation. Yet, they are vulnerable to adversarial perturbations and data poisoning attacks, particularly in tasks like text classification and translation. However, the adversarial robustness of abstractive…