  k-nearest-neighbor machine translation has demonstrated remarkable
improvements in machine translation quality by creating a datastore of cached
examples. However, these improvements have been limited to high-resource
language pairs, with large datastores, and remain a challenge for low-resource
languages. In this paper, we address this issue by combining representations
from multiple languages into a single datastore. Our results consistently
demonstrate substantial improvements not only in low-resource translation
quality (up to +3.6 BLEU), but also for high-resource translation quality (up
to +0.5 BLEU). Our experiments show that it is possible to create multilingual
datastores that are a quarter of the size, achieving a 5.3x speed improvement,
by using linguistic similarities for datastore creation.
