AIBullisharXiv – CS AI · Mar 166/10
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MetaKE: Meta-learning Aligned Knowledge Editing via Bi-level Optimization
Researchers propose MetaKE, a new framework for knowledge editing in Large Language Models that addresses the 'Semantic-Execution Disconnect' through bi-level optimization. The method treats edit targets as learnable parameters and uses a Structural Gradient Proxy to align edits with the model's feasible manifold, showing significant improvements over existing approaches.