Knowledge editing aims to adjust the knowledge within large language models (LLMs) to prevent their responses from becoming obsolete or inaccurate. However, existing works on knowledge editing are primarily conducted in a single language, which is inadequate for multilingual language models. In this paper, we focus on multilingual knowledge editing (MKE), which requires propagating updates across multiple languages. This necessity poses a significant challenge for the task. Furthermore, the limited availability of a comprehensive dataset for MKE exacerbates this challenge, hindering progress in this area. Hence, we introduce the Multilingual Knowledge Editing Benchmark (MKEB), a novel dataset comprising 12 languages and providing a complete evaluation framework. Additionally, we propose a method that enhances Multilingual knowledge Editing with neuron-Masked Low-Rank Adaptation (MEMLA). Specifically, we identify two categories of knowledge neurons to improve editing precision. Moreover, we perform LoRA-based editing with neuron masks to efficiently modify parameters and facilitate the propagation of updates across multiple languages. Experiments demonstrate that our method outperforms existing baselines and significantly enhances the multi-hop reasoning capability of the edited model, with minimal impact on its downstream task performance. The dataset and code will be made publicly available.

翻译：知识编辑旨在调整大型语言模型（LLM）内部的知识，以防止其响应变得过时或不准确。然而，现有的知识编辑工作主要在单一语言中进行，这对于多语言模型而言是不充分的。本文聚焦于多语言知识编辑（MKE），该任务要求将更新在多种语言间传播。这一需求给任务带来了显著挑战。此外，缺乏全面的MKE数据集进一步加剧了该挑战，阻碍了该领域的进展。为此，我们提出了多语言知识编辑基准（MKEB），这是一个包含12种语言的新型数据集，并提供了完整的评估框架。同时，我们提出了一种基于神经元掩码低秩适配增强多语言知识编辑的方法（MEMLA）。具体而言，我们识别了两类知识神经元以提高编辑精度。此外，我们采用基于神经元掩码的LoRA编辑来高效修改参数，并促进更新在多种语言间的传播。实验表明，我们的方法优于现有基线，显著提升了编辑后模型的多跳推理能力，且对其下游任务性能影响极小。数据集与代码将公开提供。