Knowledge Editing for Large Language Model with Knowledge Neuronal Ensemble

Understand

As real-world knowledge is constantly evolving, ensuring the timeliness and accuracy of a model's knowledge is crucial.

• This has made knowledge editing in large language models increasingly important.
• However, existing knowledge editing methods face several challenges, including parameter localization coupling, imprecise localization, and a lack of dynamic interaction across layers.
• In this paper, we propose a novel knowledge editing method called Knowledge Neuronal Ensemble (KNE).