@inproceedings{alnumay-etal-2025-command,
    title = "Command {R}7{B} {A}rabic: a small, enterprise-focused, multilingual, and culturally aware {A}rabic {LLM}",
    author = "Alnumay, Yazeed  and
      Barbet, Alexandre  and
      Bialas, Anna  and
      Darling, William  and
      Desai, Shaan  and
      Devassy, Joan  and
      Duffy, Kyle  and
      Howe, Stephanie  and
      Lasche, Olivia  and
      Lee, Justin  and
      Shrinivason, Anirudh  and
      Tracey, Jennifer",
    editor = "Lignos, Constantine  and
      Abdulmumin, Idris  and
      Adelani, David",
    booktitle = "Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.africanlp-1.17/",
    doi = "10.18653/v1/2025.africanlp-1.17",
    pages = "126--135",
    ISBN = "979-8-89176-257-2",
    abstract = "Building high-quality large language models (LLMs) for enterprise Arabic applications remains challenging due to the limited availability of digitized Arabic data. In this work, we present a data synthesis and refinement strategy to help address this problem, namely, by leveraging synthetic data generation and human-in-the-loop annotation to expand our Arabic training corpus. We further present our iterative post training recipe that is essential to achieving state-of-the-art performance in aligning the model with human preferences, a critical aspect to enterprise use cases. The culmination of this effort is the release of a small, 7B, open-weight model that outperforms similarly sized peers in head-to-head comparisons and on Arabic-focused benchmarks covering cultural knowledge, instruction following, RAG, and contextual faithfulness."
}