[2606.19346] Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer
[Submitted on 26 Apr 2026]
Title:Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer
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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.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.19346 [cs.CL]
(or arXiv:2606.19346v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.19346
arXiv-issued DOI via DataCite
Submission history
From: Ahmed Haj Ahmed [view email] [v1] Sun, 26 Apr 2026 00:59:09 UTC (54 KB)
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