@inproceedings{bhatia-etal-2025-swan,
    title = "Swan and {A}rabic{MTEB}: Dialect-Aware, {A}rabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks",
    author = "Bhatia, Gagan  and
      Nagoudi, El Moatez Billah  and
      El Mekki, Abdellah  and
      Alwajih, Fakhraddin  and
      Abdul-Mageed, Muhammad",
    editor = "Chiruzzo, Luis  and
      Ritter, Alan  and
      Wang, Lu",
    booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2025.findings-naacl.263/",
    doi = "10.18653/v1/2025.findings-naacl.263",
    pages = "4654--4670",
    ISBN = "979-8-89176-195-7",
    abstract = "In this paper, we introduce Swan, a family of embedding models centred around the Arabic language, addressing both small-scale and large-scale use cases. Swan includes two variants: Swan-Small, based on ARBERTv2, and Swan-Large, built on ArMistral, a pretrained Arabic large language model. To evaluate these models, we propose ArabicMTEB, a comprehensive benchmark suite that assesses cross-lingual, multi-dialectal, multi-domain, and multi-cultural Arabic text embedding performance, covering eight diverse tasks and spanning 94 datasets. Swan-Large achieves state-of-the-art results, outperforming Multilingual-E5-large in most Arabic tasks, while the Swan-Small consistently surpasses Multilingual-E5-base. Our extensive evaluations demonstrate that Swan models are dialectally and culturally aware, excelling across various Arabic domains while offering significant monetary efficiency. This work significantly advances the field of Arabic language modelling and provides valuable resources for future research and applications in Arabic natural language processing. Our models and benchmarks will be made publicly accessible for research."
}