XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples
arxiv(2024)
摘要
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
https://github.com/cisnlp/XAMPLER.
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