Beyond Local Edits: Embedding-Virtualized Knowledge for Broader Evaluation and Preservation of Model Editing
Abstract
Knowledge editing methods for large language models are commonly evaluated using predefined benchmarks that assess edited facts together with a limited set of related or neighboring knowledge. While effective, such evaluations remain confined to finite, dataset-bounded samples, leaving the broader impact of editing on the model's knowledge system insufficiently understood. To address this gap, we introduce Embedding-Virtualized Knowledge (EVK) that characterizes model knowledge through controlled perturbations in embedding space, enabling the exploration of a substantially broader and virtualized knowledge region beyond explicit data annotations. Based on EVK, we construct an embedding-level evaluation benchmark EVK-Bench that quantifies potential knowledge drift induced by editing, revealing effects that are not captured by conventional sample-based metrics. Furthermore, we propose a plug-and-play EVK-Align module that constrains embedding-level knowledge drift during editing and can be seamlessly integrated into existing editing methods. Experiments demonstrate that our approach enables more comprehensive evaluation while significantly improving knowledge preservation without sacrificing editing accuracy.
Cite
@article{arxiv.2602.01977,
title = {Beyond Local Edits: Embedding-Virtualized Knowledge for Broader Evaluation and Preservation of Model Editing},
author = {Shuainan Liu and Xuanang Chen and Ben He and Le Sun},
journal= {arXiv preprint arXiv:2602.01977},
year = {2026}
}
Comments
We voluntarily withdraw this manuscript. Extensive post-submission testing shows the method lacks the originally reported generality and effectiveness. The benchmark metrics originally designed are inadequate for assessing existing model editing algorithms. To avoid misleading the community, we have decided to withdraw this paper and will not release an updated version.