When English Isn't the Best Teacher: Source Language Effects in Cross-Lingual In-Context Learning
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 ta...
| Authors | Fred Philippy, Siwen Guo, Jacques Klein et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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