Title: Model Editing is Over: Revealing Its Illusory Success and Fragile Foundation
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Large language models, model editing, adversarial attack

Abstract: Large language models (LLMs) inevitably encode outdated or incorrect knowledge. Updating/deleting/forgetting such knowledge is important for alignment, safety, and other issues. To address this issue, model editing has emerged as a promising paradigm: by precisely editing a small subset of parameters such that a specific fact is updated while preserving other knowledge. Despite its great success reported in previous papers, we find the apparent reliability of editing rests on a fragile foundation and the current literature is largely driven by illusory success.  The fundamental goal of steering the model’s output toward a target with minimal modification would encourage adversarial shortcuts rather than utilizing real semantics. This problem directly challenges the feasibility of the current model editing literature at its very foundation, as adversarial shortcuts are inherently at odds with robust knowledge integration. Coincidentally, this issue has long been obscured by evaluation frameworks that lack the design of negative examples. To uncover it, we systematically develop a suite of new evaluation methods. Strikingly, we find that state-of-the-art approaches collapse even under the simplest negation queries. Our empirical evidence show that edit is likely to be based on shortcuts rather than full semantics,  calling for an urgent reconsideration of the very basis of model editing before further advancements can be meaningfully pursued.