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arXiv · 2606.19346

Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer

Ahmed Haj Ahmed·Ruochen Zhang·Alvin Grissom II
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Abstract

We study cross-lingual transfer by fine-tuning seven large language models (4B--671B parameters) on Arabic and evaluating zero-shot reading comprehension on Semitic languages and non-Semitic controls. Across dense and Mixture-of-Experts architectures, we find no evidence of Semitic-specific transfer: models with weak baselines improve dramatically across all languages, while strong-baseline models show only marginal gains regardless of language family. A chain-of-thought ablation reinforces this finding -- the same models that benefit most from fine-tuning benefit equally from inference-time reasoning, suggesting both mechanisms address task-format alignment rather than cross-lingual knowledge transfer.

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Ahmed Haj Ahmed, Ruochen Zhang, Alvin Grissom II. 2026-04-26. Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer. https://arxiv.org/abs/2606.19346

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