Recent studies have shown that leveraging off-the-shelf or fine-tuned
retrievers, capable of retrieving high-quality in-context examples,
significantly improves in-context learning of English. However, adapting these
methods to other languages, especially low-resource ones, presents challenges
due to the scarcity of available cross-lingual retrievers and annotated data.
In this paper, 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 with
positive/negative English samples, which are constructed based on the
predictions of the multilingual large language model for in-context learning.
Then, the trained retriever is directly employed to retrieve English examples
as few-shot examples for in-context learning of target languages. Experiments
on the massively multilingual text classification benchmark of SIB200 with 176
languages demonstrate that XAMPLER substantially improves the in-context
learning performance across languages. Our code is available at
this https URL

通过仅使用英语注释数据，我们介绍了一种名为 XAMPLER 的跨语言示例检索方法，用于解决跨语言上下文学习的挑战，并在 176 种语言的大规模多语言文本分类基准测试中展示了其显著改进的跨语言上下文学习性能。