MPN：利用多语言补丁神经元进行跨语言模型编辑
MPN: Leveraging Multilingual Patch Neuron for Cross-lingual Model Editing
AI总结：
针对多语言模型跨语言知识同步问题，提出训练多语言补丁神经元存储跨语言知识的方法，在XNLI和自建XFEVER数据集上验证了其能有效提升跨语言编辑性能且易于适配现有方法。
摘要：
大型语言模型以编码大量事实知识而闻名，但由于外部信息的不断变化，它们经常变得过时。应对这一挑战的一个有前景的解决方案是利用模型编辑方法以高效的方式更新知识。然而，大多数现有的模型编辑技术仅限于单语框架，因此未能解决多语言模型跨语言知识同步的关键问题。为了解决这个问题，我们提出了一种简单而有效的方法，训练多语言补丁神经元来存储跨语言知识。它可以轻松适应现有方法，以增强其跨语言编辑能力。为了评估我们的方法，我们使用XNLI数据集和自建的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，而无需对原始方法进行过多修改，从而展示了其用户友好的特性。代码即将发布。
英文摘要：
Large language models are known for encoding a vast amount of factual knowledge, but they often becomes outdated due to the ever-changing nature of external information.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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
