HoReN: Normalized Hopfield Retrieval for Large-Scale Sequential Model Editing
Large language models encode vast factual knowledge that can become outdated or incorrect after deployment, yet retraining is prohibitively costly. This motivates lifelong model editing, which updates targeted behavior while preserving the rest of the model. Existing editors, both parameter-modifying and parameter-preserving, degrade severely as edits accumulate and struggle to generalize across paraphrases. We propose HoReN, a codebook-based parameter-preserving editor that wraps a single MLP layer with a discrete key-value memory. HoReN treats each codebook entry as both a knowledge key and a Hopfield stored pattern, retrieves edits by angular similarity on the unit hypersphere, and ref...
| Authors | Yuan Fang, Yi Xie, Xuming Ran |
|---|---|
| Primary category | cs.LG |
| Published | 2 May 2026 |
| Last updated at source | 19 May 2026 |
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