When English Isn't the Best Teacher: Source Language Effects in Cross-Lingual In-Context Learning
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Computer Science > Computation and Language
Title:When English Isn't the Best Teacher: Source Language Effects in Cross-Lingual In-Context Learning
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.
| Comments: | Accepted at 1st Workshop on Multilinguality in the Era of Large Language Models (MeLLM 2026), co-located with ACL 2026 |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.18033 [cs.CL] |
| (or arXiv:2606.18033v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.18033
arXiv-issued DOI via DataCite (pending registration)
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