XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples
<|reference_start|>XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples: 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}.<|reference_end|>
@article{lin2024xampler:,
    title={XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples},
    author={Peiqin Lin, Andr'e F. T. Martins, Hinrich Sch"utze},
    journal={arXiv preprint arXiv:2405.05116},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05116},
    primaryClass={cs.CL}
}