sequential-editing

This paper investigates the mechanisms underlying sequential knowledge editing in LLMs, showing that many regularization strategies are unnecessary and that stability emerges naturally from properly accounting for accumulated editing constraints.

This paper introduces HoReN, a parameter-preserving model editing method that uses normalized Hopfield retrieval to handle large-scale sequential updates to large language models. It addresses issues of knowledge accumulation and routing challenges, demonstrating stable performance on 50K sequential edits where prior methods degrade.