InkubaLM: A small language model for low-resource African languages

Atnafu Lambebo Tonja; Bonaventure F. P. Dossou; Jessica Ojo; Jenalea Rajab; Fadel Thior; Eric Peter Wairagala; Anuoluwapo Aremu; Pelonomi Moiloa; Jade Abbott; Vukosi Marivate; Benjamin Rosman

arXiv · 2408.17024

InkubaLM: A small language model for low-resource African languages

Atnafu Lambebo Tonja·Bonaventure F. P. Dossou·Jessica Ojo·Jenalea Rajab·Fadel Thior·Eric Peter Wairagala·Anuoluwapo Aremu·Pelonomi Moiloa·Jade Abbott·Vukosi Marivate·Benjamin Rosman

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Abstract

High-resource language models often fall short in the African context, where there is a critical need for models that are efficient, accessible, and locally relevant, even amidst significant computing and data constraints. This paper introduces InkubaLM, a small language model with 0.4 billion parameters, which achieves performance comparable to models with significantly larger parameter counts and more extensive training data on tasks such as machine translation, question-answering, AfriMMLU, and the AfriXnli task. Notably, InkubaLM outperforms many larger models in sentiment analysis and demonstrates remarkable consistency across multiple languages. This work represents a pivotal advancement in challenging the conventional paradigm that effective language models must rely on substantial resources. Our model and datasets are publicly available at https://huggingface.co/lelapa to encourage research and development on low-resource languages.

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Atnafu Lambebo Tonja, Bonaventure F. P. Dossou, Jessica Ojo, Jenalea Rajab, Fadel Thior, Eric Peter Wairagala, Anuoluwapo Aremu, Pelonomi Moiloa, Jade Abbott, Vukosi Marivate, Benjamin Rosman. 2024-08-30. InkubaLM: A small language model for low-resource African languages. https://arxiv.org/abs/2408.17024

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