English is Not All You Need: Systematically Exploring the Role of Multilinguality in LLM Post-Training

Abstract:Despite the widespread multilingual deployment of large language models, post-training pipelines remain predominantly English-centric, contributing to performance disparities across languages. We present a systematic, controlled study of the interplay between training language coverage, model scale, and task domain, based on 220 supervised fine-tuning runs on parallel translated multilingual data mixtures spanning mathematical reasoning and API calling tasks, with models up to 8B parameters. We find that English-only post-training is typically suboptimal: incorporating even a single non-English language improves both English performance and cross-lingual generalization. Increasing language diversity during post-training generally yields further gains, particularly for low-resource languages, while performance on high-resource languages tends to plateau rather than degrade. Moreover, greater language diversity enables strong zero-shot transfer to unseen languages, reducing the need for direct inclusion, though gains remain limited for typologically distant, low-resource languages.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2604.13286 [cs.CL]

(or arXiv:2604.13286v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2604.13286 Focus to learn more arXiv-issued DOI via DataCite

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