Papers by Poojitha Thota
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are vulnerable to adversarial perturbations and data poisoning attacks, especially in tasks like text classification and translation. |
| Approach: | They propose a novel approach exploiting the inherent lead bias in large language models to perform adversarial perturbations and an innovative application of influence functions to execute data poisoning attacks. |
| Outcome: | The proposed approach shows that the models under attack tend to generate extractive summaries rather than abstractive summarizations. |