arXiv:2606.19346v1 Announce Type: new 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 f
The paper provides timely evidence on the effectiveness of cross-lingual transfer in large language models, a critical area given the rapid development and deployment of LLMs.
This research suggests that models with weak baselines can significantly improve across diverse languages without specific family-based transfer, impacting LLM development and accessibility globally.
The findings challenge assumptions about language family-specific transfer, implying that effective multilingual LLMs may not require highly specialized architectural or training approaches for certain language groups.
- · Developers of less-resourced languages
- · Global AI adoption
- · Large language model developers
- · Niche language-specific AI development strategies
Cross-lingual transfer capabilities of large language models are further elucidated, impacting their trainability and deployment.
This could lead to more rapid development of AI applications in a wider array of languages, especially those outside dominant linguistic groups.
Increased accessibility and performance of AI in diverse linguistic contexts could accelerate global digital inclusion and potentially shift power dynamics in AI development away from English-centric models.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL