2024/10/26 by Poojitha Thota, Shirin Nilizadeh, Thota, Poojitha +1 · 1 citation
Computer Science · Decision Sciences · #Computation and Language (cs.CL) #Cryptography and Security (cs.CR) #Data Quality and Management #FOS: Computer and information sciences #Natural Language Processing Techniques #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2410.20019
openalex publication_date 2024/10/26 · openalex created_date 2024/11/14 · openalex updated_date 2026/07/28
Large Language Models 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 text summarization models remains less explored. In this work, we unveil a novel approach by exploiting the inherent lead bias in summarization models, to perform adversarial perturbations. Furthermore, we introduce an innovative application of influence functions, to execute data poisoning, which compromises the model's integrity. This approach not only shows a skew in the models behavior to produce desired outcomes but also shows a new behavioral change, where models under attack tend to generate extractive summaries rather than abstractive summaries.