HoReN: Normalized Hopfield Retrieval for Large-Scale Sequential Model Editing
📰 ArXiv cs.AI
Learn how HoReN enables efficient sequential model editing for large language models, updating targeted behavior without retraining
Action Steps
- Apply HoReN to update targeted behavior in large language models
- Use locate-then-edit procedures to install new facts
- Evaluate the impact of accumulated edits on original knowledge
- Configure HoReN for normalized Hopfield retrieval
- Test the efficiency of HoReN in sequential model editing
Who Needs to Know This
AI researchers and engineers working on large language models can benefit from HoReN to update models without retraining, while preserving original knowledge
Key Insight
💡 HoReN enables targeted updates to large language models without harming original knowledge, reducing the need for costly retraining
Share This
🚀 HoReN: Efficient sequential model editing for large language models without retraining! 💡
Key Takeaways
Learn how HoReN enables efficient sequential model editing for large language models, updating targeted behavior without retraining
Full Article
Title: HoReN: Normalized Hopfield Retrieval for Large-Scale Sequential Model Editing
Abstract:
arXiv:2605.08143v1 Announce Type: cross Abstract: Large language models encode vast factual knowledge that inevitably becomes outdated or incorrect after deployment, yet retraining is costly prohibitive, motivating model editing in lifelong settings that updates targeted behavior without harming the rest of the model. One line of work installs new facts by directly modifying base weights through locate-then-edit procedures, but accumulated edits progressively disrupt originally preserved knowled
Abstract:
arXiv:2605.08143v1 Announce Type: cross Abstract: Large language models encode vast factual knowledge that inevitably becomes outdated or incorrect after deployment, yet retraining is costly prohibitive, motivating model editing in lifelong settings that updates targeted behavior without harming the rest of the model. One line of work installs new facts by directly modifying base weights through locate-then-edit procedures, but accumulated edits progressively disrupt originally preserved knowled
DeepCamp AI