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February 4, 2025
Large language models (LLMs) have made impressive progress in recent years and are used in a variety of areas, from text generation and translation to answering questions. A crucial aspect for the effective use of these models is the ability to update their knowledge and adapt to new information. So-called "Knowledge Editing" allows specific facts and information to be changed in LLMs without having to retrain the entire model. This is particularly important in dynamic environments where information changes rapidly.
However, previous approaches to knowledge editing, especially the so-called "Locate-then-Edit" methods, encounter difficulties with the sequential editing of larger amounts of knowledge. Studies have shown that the repeated application of these methods can lead to a significant deterioration of model performance, a phenomenon known as "Model Degradation".
The causes of this degradation are manifold. Firstly, "Locate-then-Edit" methods tend to overemphasize the edited facts, leading to overfitting on this specific information. Secondly, the continuous application of these methods leads to a disproportionate increase in the norm of the edited matrices within the model. This norm increase, as current research shows, is a mechanism by which the edited layers within the model gain disproportionately large importance for the model's output. This can lead to the model correctly reproducing the edited facts, but losing performance in other areas.
To address these challenges, new approaches have been developed that enable more robust and scalable knowledge editing. One promising approach is ENCORE (Early stopping and Norm-Constrained Robust knowledge Editing). ENCORE relies on two core mechanisms to address the problems of overfitting and excessive norm growth: First, early stopping of the editing process is implemented to minimize overfitting on the edited facts. Second, the norm growth of the edited matrices is controlled by a special constraint. Through these measures, ENCORE can perform a high number of sequential edits without affecting the overall performance of the model. Tests have shown that with ENCORE up to 10,000 sequential edits are possible without significantly reducing the downstream performance of the original model.
Furthermore, ENCORE also offers advantages in terms of editing speed. Compared to existing methods such as MEMIT and AlphaEdit, ENCORE was able to achieve significantly higher speeds when editing large language models like Llama3-8B.
The development of robust and efficient methods for knowledge editing is a crucial step in realizing the full potential of LLMs. Approaches like ENCORE open up new possibilities for the continuous updating and adaptation of LLMs to dynamic information landscapes and contribute to improving the performance and reliability of these models in practice.
