Impact of Vocabulary Expansion versus Continued Pre-training on XLM-R Adaptation to Low-Resource African Languages

Abstract

Abstract: Multilingual LLMs support a variety of languages; however, their performance is suboptimal for low-resource languages. In this work, we emphasize the importance of continued pre-training of multilingual LLMs and the use of translation-based synthetic pre-training corpora for improving LLMs in low-resource languages. We conduct our study in the context of the low-resource Indic language Hindi. We introduce Nemotron-Mini-Hindi 4B, a bilingual SLM supporting both Hindi and English, based on Nemotron-Mini 4B. The model is trained using a mix of real and synthetic Hindi + English tokens, with conti

Research Question

What is the impact of vocabulary expansion versus continued pre-training on the convergence speed and final accuracy of XLM-R when adapting to low-resource African languages?

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