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

• URL: http://arxiv.org/abs/2408.17024v2
• Date: Tue, 3 Sep 2024 13:55:01 GMT
• Title: InkubaLM: A small language model for low-resource African languages
• Authors: 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,
• Abstract summary: InkubaLM is a small language model with 0.4 billion parameters. It achieves performance comparable to models with significantly larger parameter counts. It demonstrates remarkable consistency across multiple languages.
• Score: 9.426968756845389
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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