Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer
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Computer Science > Computation and Language
Title:Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer
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
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