XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples

• URL: http://arxiv.org/abs/2405.05116v3
• Date: Sat, 08 Feb 2025 10:44:14 GMT
• Title: XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples
• Authors: Peiqin Lin, André F. T. Martins, Hinrich Schütze,
• Abstract summary: We introduce XAMPLER: Cross-Lingual Example Retrieval, a method tailored to tackle the challenge of cross-lingual in-context learning. XAMPLER first trains a retriever based on Glot500, a multilingual small language model. It can directly retrieve English examples as few-shot examples for in-context learning of target languages.
• Score: 64.79218405438871
• License:
• Abstract: 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 two multilingual text classification benchmarks, namely SIB200 with 176 languages and MasakhaNEWS with 16 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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