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

Year: 2026

Type: conference-paper

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.

Source: Workshop on Multilinguality in the Era of Large Language Models (MeLLM)

DOI: 10.18653/v1/2026.mellm-1.31

Authors: Fred Philippy, Siwen Guo, Jacques Klein, Tegawendé F. Bissyandé

Institutions: University of Luxembourg, Luxembourg Institute of Science and Technology

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7166805647

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