MPN: Leveraging Multilingual Patch Neuron for Cross-lingual Model Editing
This addresses cross-lingual knowledge synchronization for multilingual models, which is an incremental improvement over monolingual editing techniques.
The paper tackles the problem of outdated factual knowledge in multilingual language models by proposing a method to train multilingual patch neurons for cross-lingual model editing, achieving improved performance on datasets like XNLI and XFEVER without major modifications to existing approaches.
Large language models are known for encoding a vast amount of factual knowledge, but they often becomes outdated due to the ever-changing nature of external information.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.