Exposing the Illusion of Erasure in Knowledge Editing for LLMs

arXiv:2606.23276v2 Announce Type: replace Abstract: Knowledge Editing (KE) has emerged as a frontier for updating specific facts in LLMs without costly retraining, but its reliability and underlying mechanisms remain poorly understood. In this work, we examine KE from an adversarial elicitation perspective, revealing that edited knowledge is often not fully erased and continues to surface, with consistent failures observed across diverse model architectures. To explain this behavior, we conduct a mechanistic analysis of popular KE methods. We show that low-rank updates do not overwrite existin

Source: arXiv cs.LG — read the full report at the original publisher.

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