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knowledge, but they often becomes outdated due to the ever-changing nature of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.

提出了一种简单而有效的方法，使用多语言修补神经元来存储跨语言知识，以提升现有方法的跨语言编辑能力，并在两个数据集上进行实验证明其在跨语言编辑任务中表现出了提高的性能。