  Low-resource African languages pose unique challenges for natural language
processing (NLP) tasks, including natural language generation (NLG). In this
paper, we develop Cheetah, a massively multilingual NLG language model for
African languages. Cheetah supports 517 African languages and language
varieties, allowing us to address the scarcity of NLG resources and provide a
solution to foster linguistic diversity. We demonstrate the effectiveness of
Cheetah through comprehensive evaluations across six generation downstream
tasks. In five of the six tasks, Cheetah significantly outperforms other
models, showcasing its remarkable performance for generating coherent and
contextually appropriate text in a wide range of African languages. We
additionally conduct a detailed human evaluation to delve deeper into the
linguistic capabilities of Cheetah. The introduction of Cheetah has
far-reaching benefits for linguistic diversity. By leveraging pretrained models
and adapting them to specific languages, our approach facilitates the
development of practical NLG applications for African communities. The findings
of this study contribute to advancing NLP research in low-resource settings,
enabling greater accessibility and inclusion for African languages in a rapidly
expanding digital landscape. We publicly release our models for research.
