Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer
Quick Answer
This study investigates cross-lingual transfer in seven large language models (4B-671B parameters) fine-tuned on Arabic, revealing no Semitic-specific transfer.
Quick Take
Models with weak baselines showed significant improvements across languages, while strong baselines had marginal gains, indicating task-format alignment rather than cross-lingual knowledge transfer.
Key Points
- Seven (4B-671B parameters) were fine-tuned on Arabic.
- No evidence of Semitic-specific transfer was found across language families.
- Weak baseline models improved significantly; strong baselines showed marginal gains.
- Inference-time reasoning benefited models equally, indicating task-format alignment.
- Study reinforces the importance of task alignment over cross-lingual knowledge transfer.
Paper Resources
📖 Reader Mode
~1 min readAbstract: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)
— Originally published at arxiv.org
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