Large language models are known for encoding a vast amount of factual knowledge, but they often becomes outdated due to the ever-changing nature of external information. A promising solution to this challenge is the utilization of model editing methods to update the knowledge in an efficient manner. However, the majority of existing model editing techniques are limited to monolingual frameworks, thus failing to address the crucial issue of cross-lingual knowledge synchronization for multilingual models. To tackle this problem, we propose a simple yet effective method that trains multilingual patch neuron to store cross-lingual knowledge. It can be easily adapted to existing approaches to enhance their cross-lingual editing capabilities. To evaluate our method, we conduct experiments using both the XNLI dataset and a self-constructed XFEVER dataset. Experimental results demonstrate that our proposed method achieves improved performance in cross-lingual editing tasks without requiring excessive modifications to the original methodology, thereby showcasing its user-friendly characteristics. Codes will be released soon.

翻译：大型语言模型以编码大量事实知识而闻名，但受外部信息不断变化的特性影响，这些知识常常会过时。应对这一挑战的一个有前景的方案是利用模型编辑方法以高效方式更新知识。然而，现有的大多数模型编辑技术局限于单语言框架，因此未能解决多语言模型中跨语言知识同步这一关键问题。为解决此问题，我们提出了一种简单而有效的方法，即训练多语言补丁神经元以存储跨语言知识。该方法可轻松适配现有方法，以增强其跨语言编辑能力。为评估我们的方法，我们使用XNLI数据集和自行构建的XFEVER数据集进行了实验。实验结果表明，我们提出的方法在跨语言编辑任务中实现了性能提升，且无需对原有方法进行过多修改，从而展现了其用户友好的特性。代码将很快发布。