@inproceedings{uemura-etal-2024-afriinstruct,
    title = "{A}fri{I}nstruct: Instruction Tuning of {A}frican Languages for Diverse Tasks",
    author = "Uemura, Kosei  and
      Chen, Mahe  and
      Pejovic, Alex  and
      Maduabuchi, Chika  and
      Sun, Yifei  and
      Lee, En-Shiun Annie",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.findings-emnlp.793/",
    doi = "10.18653/v1/2024.findings-emnlp.793",
    pages = "13571--13585",
    abstract = "Large language models (LLMs) for African languages perform worse compared to their performance in high-resource languages. To address this issue, we introduce AfriInstruct, which specializes in instruction-tuning of multiple African languages covering various tasks. We trained the LLaMa-2-7B using continual pretraining and instruction fine-tuning, which demonstrates superior performance across multiple tasks. Our mixed task evaluation shows that our model outperforms GPT-3.5-Turbo and other baseline models of similar size. Our contributions fill a critical gap of LLM performance between high-resource and African languages."
}