Papers by Sherif Saad
Preserving Privacy Through Dememorization: An Unlearning Technique For Mitigating Memorization Risks In Language Models (2023.emnlp-main)
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| Challenge: | Large Language models (LLMs) are trained on vast amounts of data, including sensitive information that poses a risk to personal privacy if exposed. |
| Approach: | They propose a novel unlearning approach that utilizes an efficient reinforcement learning feedback loop via proximal policy optimization to incentivize the LLMs to learn a paraphrasing policy to unlearn the pre-training data. |
| Outcome: | The proposed approach surpasses strong baselines and state-of-the-art methods in terms of its ability to generalize and strike a balance between privacy and LLM performance. |
ALPACA AGAINST VICUNA: Using LLMs to Uncover Memorization of LLMs (2025.naacl-long)
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Aly M. Kassem, Omar Mahmoud, Niloofar Mireshghallah, Hyunwoo Kim, Yulia Tsvetkov, Yejin Choi, Sherif Saad, Santu Rana
| Challenge: | Existing studies have shown that pre-trained LLMs emit training data up to 150 more often than in regular operation. |
| Approach: | They propose a black-box prompt optimization method where an attacker LLM agent uncovers higher levels of memorization in a victim agent . |
| Outcome: | The proposed method shows 23.7% more overlap with training data compared to state-of-the-art baselines. |
Finding a Needle in the Adversarial Haystack: A Targeted Paraphrasing Approach For Uncovering Edge Cases with Minimal Distribution Distortion (2024.eacl-long)
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| Challenge: | Adversarial attacks against Language models (LMs) are a significant concern. |
| Approach: | They propose an approach to automatically learn a policy to generate challenging examples that improve the model’s performance. |
| Outcome: | The proposed approach outperforms baselines and exhibits generalizability across classifiers and datasets. |