Large language models (LLMs) often suffer from outdated or incorrect knowledge, prompting ongoing research into efficient model editing . Existing methods, however, mainly target individual knowledge facts. When multiple facts need to be edited in a coherent sequence, they frequently lead to deviations or even breakdowns in model's general abilities. This problem intensifies in batch-sequential editing, where multiple facts are updated simultaneously, compared to single-sequential editing. In this work, by analyzing the parameter matrix, we identify that the degradation stems from unintended modifications that should ideally remain unaffected. These changes accumulate with the number and batch size of edits, ultimately harming editing performance and general abilities. To address this, we propose B atch-Aware E diting A nchor C ompression (B-EAC), a framework tailored for sequential model editing. B-EAC dynamically selects essential anchors for each edit while compressing the influence on nearby parameters. It adopts a layer-wise anchor selection strategy to prevent anchor conflicts during concurrent edits and introduces a rolling anchor refresh mechanism to enhance adaptability across batches. Experiments conducted on three LLMs across four tasks demonstrate that B-EAC effectively suppresses deviation during model editing, achieving a 36.54% performance improvement compared to the case without it. Our work offers a practical and theoretically grounded framework for updating LLMs efficiently, paving the way for continual knowledge refinement in real-world applications.
Xu et al. (2026) studied this question.