Papers by Aly M. Kassem
REVIVING YOUR MNEME: Predicting The Side Effects of LLM Unlearning and Fine-Tuning via Sparse Model Diffing (2025.emnlp-main)
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| Challenge: | Existing evaluation methods assess performance after LLMs are fine-tuned or unlearned to adapt to new tasks or eliminate undesirable behaviors. |
| Approach: | They propose a framework for identifying unintended side effects using sparse model diffing. |
| Outcome: | The proposed framework can detect unintended side effects without fine-tuning data . it achieves 95% accuracy in predicting side effects, aligning with known benchmarks . |
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. |
How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities (2026.eacl-long)
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| Challenge: | Large language model (LLM) routing has emerged as a promising solution to balancing computational costs and performance. |
| Approach: | They propose a framework that categorizes router performance across a broad spectrum of query types . large language models have revolutionized natural language processing . |
| Outcome: | The proposed framework categorizes router performance across a broad spectrum of query types . it integrates privacy and safety assessments to reveal hidden risks . |