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cs.CL2026
Detect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning
Poojitha Thota, Shirin Nilizadeh
Training-time data poisoning during fine-tuning poses a significant threat to large language models (LLMs) deployed for abstractive text summarization, where small task-specific da…
cs.CL2024★ 1 cited
Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions
Poojitha Thota, Shirin Nilizadeh
Large Language Models have introduced novel opportunities for text comprehension and generation. Yet, they are vulnerable to adversarial perturbations and data poisoning attacks, p…
cs.CL2021
Attacks against Ranking Algorithms with Text Embeddings: a Case Study on Recruitment Algorithms
Anahita Samadi, Debapriya Banerjee, Shirin Nilizadeh
Recently, some studies have shown that text classification tasks are vulnerable to poisoning and evasion attacks. However, little work has investigated attacks against decision mak…