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arXiv · 2606.18033

When English Isn't the Best Teacher: Source Language Effects in Cross-Lingual In-Context Learning

Fred Philippy·Siwen Guo·Jacques Klein·Tegawendé F. Bissyandé
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Abstract

Cross-lingual transfer in multilingual NLP has been widely explored in supervised fine-tuning contexts, where factors like data availability and linguistic similarity largely determine transfer quality. As the field shifts toward few-shot In-Context Learning (ICL), it is often presumed that insights from fine-tuning carry over unchanged. Yet this assumption has not been rigorously evaluated, leaving open the question of how to choose source languages for cross-lingual ICL. We conduct a broad empirical study of cross-lingual transfer in ICL spanning seven tasks, six models, and a typologically diverse set of languages. We further analyze language confusion, a key obstacle for generative tasks in cross-lingual ICL. Our results show that conventional fine-tuning-based expectations do not consistently apply in the ICL regime and point to alternative heuristics for selecting source languages effectively.

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Fred Philippy, Siwen Guo, Jacques Klein, Tegawendé F. Bissyandé. 2026-06-16. When English Isn't the Best Teacher: Source Language Effects in Cross-Lingual In-Context Learning. https://arxiv.org/abs/2606.18033

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