@inproceedings{gupta-etal-2025-lifelong,
    title = "Lifelong Knowledge Editing requires Better Regularization",
    author = "Gupta, Akshat  and
      Prateepamornkul, Phudish  and
      Lu, Maochuan  and
      Alaa, Ahmed  and
      Hartvigsen, Thomas  and
      Anumanchipalli, Gopala",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1234/",
    doi = "10.18653/v1/2025.findings-emnlp.1234",
    pages = "22653--22675",
    ISBN = "979-8-89176-335-7",
    abstract = "Knowledge editing is a promising way to improve factuality in large language models, but recent studies have shown significant model degradation during sequential editing. In this paper, we formalize the popular locate-then-edit methods as a two-step fine-tuning process, allowing us to precisely identify the root cause of this degradation. We show that model degradation occurs due to (1) over-optimization of internal activations and (2) continuous norm-growth of edited matrices. To mitigate these issues, we introduce two regularization techniques: (1) Most-Probable Early Stopping (MPES) and (2) explicit Frobenius norm-constraint. We demonstrate that applying these simple yet effective regularization techniques at key points in the editing process can substantially mitigate model degradation. Combining these regularization methods enables scaling locate-then-edit methods to 10,000 edits while reducing editing time by 42-61{\%}. These results show that targeted regularization is essential for lifelong knowledge editing."
}