Feeding BabyLMs Macaroni: Code-Switching Curricula Cause Cross-Lingual Convergence

Abstract:Children in multilingual communities often code-switch, using multiple languages in a single utterance. Can we induce cross-lingual alignment in language models by training on code-switched text? We pretrain small decoder-only transformers on two 100M-word multilingual corpora: a base corpus formed by mixing the English, Dutch, and Chinese BabyBabelLM datasets, and a corpus generated from it by inserting word- and sentence-level code-switching using an LLM. We find that training on code-switched data aligns the representations of parallel text, particularly across different scripts, and that this alignment persists through training on monolingual documents. Under a learning curriculum that progresses from word-level code-switching, to sentence-level code-switching, to monolingual documents, models trained on code-switched data outperform baselines trained without it on the BabyLM evaluation suite. Our work characterizes code-switching curriculum learning as an effective data augmentation method for multilingual pretraining. We release our code, data, and models at this https URL.

17 pages, 8 figures. Accepted to the BabyLM Workshop at EMNLP 2026

Computation and Language (cs.CL)

arXiv:2609.30535 [cs.CL]

(or arXiv:2609.30535v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.30535 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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