Semantic CodeSwitching for LowResource Language Adaptation

Abstract

Cross-lingual adaptation remains a significant challenge for Large Language Models (LLMs) and Vision-Language Models (VLMs), particularly for low-resource languages. While the synthetic generation of code-switched text is an effective cross-lingual adaptation strategy, stochastic word replacement often degrades semantic integrity. We propose Semantic Code-Switching (SCS), a framework ensuring high-fidelity cross-lingual augmentation. We first train a Semantic Alignment Scorer to predict contextual similarity between matrix and code-switched sentences, then integrate this scorer into training pipelines via temperature-scaled sampling. Across diverse tasks, SCS consistently outperforms baselines like CoSDA-ML. Our method is especially robust for typologically distant languages, offering a scalable solution for multilingual adaptation without requiring parallel corpora.