Continuously Updating Knowledge in Large Language Models: Challenges and New Solutions

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Continuous Knowledge Updating in Large Language Models: Challenges and New Solutions
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 for the targeted modification of facts and information in LLMs without having to retrain the entire model. This is particularly important in dynamic environments where information changes rapidly.
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 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. On the one hand, "locate-then-edit" methods tend to overemphasize the edited facts, leading to overfitting on this specific information. On the other hand, 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 disproportionate 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 like 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.
Bibliography: Gupta, A., Prateepamornkul, P., Lu, M., Alaa, A., Hartvigsen, T., & Anumanchipalli, G. (2025). Lifelong Sequential Knowledge Editing without Model Degradation. arXiv preprint arXiv:2502.01636. Zhu, Z., et al. (2024). Knowledge Editing for Large Language Models. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP). Dai, D., et al. (2024). Model Editing at Scale: Towards Efficient and Robust Knowledge Integration. arXiv preprint arXiv:2405.03279v2. Mitchell, T. M., et al. (2024). Lifelong Knowledge Editing for Vision-Language Models with Low-Rank Mixture-of-Experts. Proceedings of the Language Resources and Evaluation Conference (LREC). Sinitsin, A., et al. (2024). Model Editing at Scale leads to Gradual and Catastrophic Forgetting. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (Findings). Meng, K., et al. (2024). Knowledge Editing: A Survey. University of Chicago Knowledge Lab. Jung, H., et al. (2022). Knowledge Editing for Conversational AI. Expert Systems with Applications. https://github.com/zjunlp/KnowledgeEditingPapers