  The training of large language models (LLMs) necessitates substantial data
and computational resources, and updating outdated LLMs entails significant
efforts and resources. While numerous model editing techniques (METs) have
emerged to efficiently update model outputs without retraining, their
effectiveness in multilingual LLMs, where knowledge is stored in diverse
languages, remains an underexplored research area. This research paper
introduces the cross-lingual model editing (\textbf{XME}) paradigm, wherein a
fact is edited in one language, and the subsequent update propagation is
observed across other languages. To investigate the XME paradigm, we conducted
experiments using BLOOM, mBERT, and XLM-RoBERTa using the two writing scripts:
\textit{Latin} (English, French, and Spanish) and \textit{Indic} (Hindi,
Gujarati, and Bengali). The results reveal notable performance limitations of
state-of-the-art METs under the XME setting, mainly when the languages involved
belong to two distinct script families. These findings highlight the need for
further research and development of XME techniques to address these challenges.
For more comprehensive information, the dataset used in this research and the
associated code are publicly available at the following
URL\url{https://github.com/lingo-iitgn/XME}.
