MetaKE: Meta-Learning for Knowledge Editing Toward a Better Accuracy-Editability Trade-off
Existing locate-then-edit Knowledge Editing (KE) methods typically decompose editing into two stages: upstream target representation optimization and downstream constrained parameter optimization. The optimization across the two stages is disconnected: upstream applies uniform regularization without observing downstream realization of the planned residual, hindering a refined accuracy-editability trade-off. Since this realization is request-specific and depends on downstream constraints, uniform regularization can over-shrink high-association requests, causing insufficient editing, while it can under-regularize low-association requests, producing over-large planned residuals that reduce d...
| Authors | Shuxin Liu, Di Gao, Ou Wu |
|---|---|
| Primary category | cs.CL |
| Published | 13 March 2026 |
| Last updated at source | 7 May 2026 |
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