@inproceedings{ahmed-etal-2026-disentangling,
    title = "Disentangling Linguistic Relatedness from Task Alignment in Cross-Lingual Transfer",
    author = "Ahmed, Ahmed Haj  and
      Zhang, Ruochen  and
      Grissom II, Alvin C",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-srw.62/",
    pages = "683--700",
    ISBN = "979-8-89176-393-7",
    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."
}