  Large language models (LLMs) possess extensive parametric knowledge, but this
knowledge is difficult to update with new information because retraining is
very expensive and infeasible for closed-source models. Knowledge editing (KE)
has emerged as a viable solution for updating the knowledge of LLMs without
compromising their overall performance. On-the-fly KE methods, inspired by
in-context learning (ICL), have shown great promise and allow LLMs to be
treated as black boxes. In the past, KE was primarily employed in English
contexts, whereas the potential for cross-lingual KE in current English-centric
LLMs has not been fully explored. To foster more research in this direction, we
introduce the BMIKE-53 benchmark for evaluating cross-lingual KE on 53 diverse
languages across three KE task types. We also propose a gradient-free KE method
called Multilingual In-context Knowledge Editing (MIKE) and evaluate it on
BMIKE-53. Our evaluation focuses on cross-lingual knowledge transfer in terms
of reliability, generality, locality, and portability, offering valuable
insights and a framework for future research in cross-lingual KE. Our code and
data are publicly accessible via the anonymous repository at
https://anonymous.4open.science/r/MIKE.
