Adapting Multilingual LLMs to Low-Resource Languages using Continued Pre-training and Synthetic Corpus

Raviraj Joshi; Kanishk Singla; Anusha Kamath; Raunak Kalani; Rakesh Paul; Utkarsh Vaidya; Sanjay Singh Chauhan; Niranjan Wartikar; Eileen Long

arXiv · 2410.14815

Adapting Multilingual LLMs to Low-Resource Languages using Continued Pre-training and Synthetic Corpus

Raviraj Joshi·Kanishk Singla·Anusha Kamath·Raunak Kalani·Rakesh Paul·Utkarsh Vaidya·Sanjay Singh Chauhan·Niranjan Wartikar·Eileen Long

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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 continuous pre-training performed on 400B tokens. We demonstrate that both the base and instruct models achieve state-of-the-art results on Hindi benchmarks while remaining competitive on English tasks. Additionally, we observe that the continued pre-training approach enhances the model's overall factual accuracy. We perform an ablation study to highlight the impact of Hindi pre-training, showing significant improvements in Hindi chat capabilities and factual accuracy, which cannot be achieved through Hindi alignment alone.

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Raviraj Joshi, Kanishk Singla, Anusha Kamath, Raunak Kalani, Rakesh Paul, Utkarsh Vaidya, Sanjay Singh Chauhan, Niranjan Wartikar, Eileen Long. 2025-04-21. Adapting Multilingual LLMs to Low-Resource Languages using Continued Pre-training and Synthetic Corpus. https://arxiv.org/abs/2410.14815

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