When English Isn't the Best Teacher: Source Language Effects in Cross-Lingual In-Context Learning
For practitioners of multilingual NLP, this work challenges assumptions about cross-lingual transfer in ICL and provides practical guidance for source language selection.
This paper investigates cross-lingual transfer in In-Context Learning (ICL) and finds that conventional fine-tuning-based expectations do not consistently apply, leading to alternative heuristics for source language selection.
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.