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

• URL: http://arxiv.org/abs/2606.18033v1
• Date: Tue, 16 Jun 2026 15:09:42 GMT
• Title: When English Isn't the Best Teacher: Source Language Effects in Cross-Lingual In-Context Learning
• Abstract summary: Cross-lingual transfer in multilingual NLP has been widely explored in supervised finetuning contexts.<n>As the field shifts toward few-shot In-Context Learning (ICL), it is often presumed that insights from fine-tuning carry over unchanged.<n>We conduct a broad empirical study of cross-lingual transfer in ICL spanning seven tasks, six models, and a typologically diverse set of languages.
• Score: 11.937949915786719
• License: http://creativecommons.org/licenses/by/4.0/
• 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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