A MEMIT-style knowledge editing framework for MoE LLMs that formulates per-expert updates via tensor structure and applies Woodbury identity for low-rank inversions, achieving up to 6x speedup with comparable editing quality.
A unified framework for model editing
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
CrispEdit edits LLMs via low-curvature projections using Bregman divergence and K-FAC approximations, achieving high edit success with under 1% average capability degradation.
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Scalable Knowledge Editing for Mixture-of-Experts LLMs via Tensor-Structured Updates
A MEMIT-style knowledge editing framework for MoE LLMs that formulates per-expert updates via tensor structure and applies Woodbury identity for low-rank inversions, achieving up to 6x speedup with comparable editing quality.
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CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing
CrispEdit edits LLMs via low-curvature projections using Bregman divergence and K-FAC approximations, achieving high edit success with under 1% average capability degradation.