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

arXiv:2606.18033v1 Announce Type: new 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 spanni

Source: arXiv cs.CL — read the full report at the original publisher.

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