  Large language models are often expected to constantly adapt to new sources
of knowledge and knowledge editing techniques aim to efficiently patch the
outdated model knowledge, with minimal modification. Most prior works focus on
monolingual knowledge editing in English, even though new information can
emerge in any language from any part of the world. We propose the Cross-Lingual
Multi-Hop Knowledge Editing paradigm, for measuring and analyzing the
performance of various SoTA knowledge editing techniques in a cross-lingual
setup. Specifically, we create a parallel cross-lingual benchmark,
CROLIN-MQUAKE for measuring the knowledge editing capabilities. Our extensive
analysis over various knowledge editing techniques uncover significant gaps in
performance between the cross-lingual and English-centric setting. Following
this, we propose a significantly improved system for cross-lingual multi-hop
knowledge editing, CLEVER-CKE. CLEVER-CKE is based on a retrieve, verify and
generate knowledge editing framework, where a retriever is formulated to recall
edited facts and support an LLM to adhere to knowledge edits. We develop
language-aware and hard-negative based contrastive objectives for improving the
cross-lingual and fine-grained fact retrieval and verification process used in
this framework. Extensive experiments on three LLMs, eight languages, and two
datasets show CLEVER-CKE's significant gains of up to 30% over prior methods.
