  Recent studies indicate that leveraging off-the-shelf or fine-tuned
retrievers, capable of retrieving relevant in-context examples tailored to the
input query, enhances few-shot in-context learning of English. However,
adapting these methods to other languages, especially low-resource ones, poses
challenges due to the scarcity of cross-lingual retrievers and annotated data.
Thus, we introduce XAMPLER: Cross-Lingual Example Retrieval, a method tailored
to tackle the challenge of cross-lingual in-context learning using only
annotated English data. XAMPLER first trains a retriever based on Glot500, a
multilingual small language model, using positive and negative English examples
constructed from the predictions of a multilingual large language model, i.e.,
MaLA500. Leveraging the cross-lingual capacity of the retriever, it can
directly retrieve English examples as few-shot examples for in-context learning
of target languages. Experiments on the multilingual text classification
benchmark SIB200 with 176 languages show that XAMPLER substantially improves
the in-context learning performance across languages. Our code is available at
\url{https://github.com/cisnlp/XAMPLER}.
