Prompt-time Cross-lingual Transfer for Low-resource Languages
Year: 2026
Type: article
Abstract: Large language models (LLMs) can translate low-resource (LR) languages from a prompt alone, but whether this reflects genuine cross-lingual transfer or surface-cuedriven task recognition remains unclear. Using a 10-language subset of the LoResLM “Rosetta Stone” benchmark in a controlled regime where in-context demonstrations are high-resource (HR)!EN pairs and the LR language appears only in the final query, I evaluate four multilingual LLMs across six prompt conditions. Two findings qualify the linguistically diverse prompting recommendation: a single well-chosen close donor matches or outperforms typologically diverse mixtures, and close donors beat far donors in 82.5% of language–model cells (+5:0 chrF++). A mixed-effects regression over 12,320 observations identifies character trigram overlap—not genealogical distance—as the strongest predictor of translation quality. The results support interpreting prompt-time “transfer” as task location and surface-form activation. Practical guideline: prefer donors with high character level overlap over typologically diverse ones.
Source: UNC Libraries
DOI: 10.17615/531k-6q91
Author: Stakovskii, Elisei
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7203872456
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