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arXiv · 2610.06161

Introducing Code-Switched Contexts to Cognitively-Inspired Bilingual Model Training

Zhuojing Huang·Luise Pohlmann·Lisa Beinborn
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Abstract

During language acquisition, bilingual children are regularly exposed to code-switched input and use it as a cognitive scaffold to accelerate vocabulary growth and cross-linguistic syntactic mapping. In contrast, computational bilingual models are conventionally pretrained on interleaved monolingual corpora. While introducing synthetic code-switching during pretraining has become a promising strategy to enhance cross-lingual alignment and downstream performance, the structural and developmental parameters governing the success remain poorly understood. In this work, we investigate the efficiency of training with synthetic code-switched data across two typologically distinct language pairs by controlling two key variables: the structural location of code-switches and the dynamic switching rate across training stages. Our results show that training with code-switched data improves cross-lingual alignment for typologically close languages.

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Zhuojing Huang, Luise Pohlmann, Lisa Beinborn. 2026-10-05. Introducing Code-Switched Contexts to Cognitively-Inspired Bilingual Model Training. https://arxiv.org/abs/2610.06161

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