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.
@article{arxiv.2502.01636,
title = {Lifelong Knowledge Editing requires Better Regularization},
author = {Akshat Gupta and Phudish Prateepamornkul and Maochuan Lu and Ahmed Alaa and Thomas Hartvigsen and Gopala Anumanchipalli},
journal= {arXiv preprint arXiv:2502.01636},
year = {2025}
}