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cs.CL2025
What the "Spotless" Mind Remembers: How Knowledge Entanglement Shapes What Leaks After Unlearning in LLMs
Aakriti Shah, Yifan Hu, Thai Le
Unlearning in large language models (LLMs) is usually evaluated as whether an "unlearned" fact can be recovered. We instead ask whether a fact's structural entanglement with the re…
cs.CL2025
Harry Potter is Still Here! Probing Knowledge Leakage in Targeted Unlearned Large Language Models via Automated Adversarial Prompting
Bang Trinh Tran To, Thai Le
This work presents LURK (Latent UnleaRned Knowledge), a novel framework that probes for hidden retained knowledge in unlearned LLMs through adversarial suffix prompting. LURK autom…
cs.LG2025
Towards Robust and Accurate Stability Estimation of Local Surrogate Models in Text-based Explainable AI
Christopher Burger, Charles Walter, Thai Le +1
Recent work has investigated the concept of adversarial attacks on explainable AI (XAI) in the NLP domain with a focus on examining the vulnerability of local surrogate methods suc…