@inproceedings{kadaoui-etal-2026-jeem,
    title = "{JEEM}: Vision-Language Understanding in Four {A}rabic Dialects",
    author = "Kadaoui, Karima  and
      Atwany, Hanin  and
      Al-Ali, Hamdan  and
      Mohamed, Abdelrahman  and
      Mekky, Ali  and
      Tilga, Sergei  and
      Fedorova, Natalia  and
      Artemova, Ekaterina  and
      Aldarmaki, Hanan  and
      Kementchedjhieva, Yova",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.18/",
    pages = "331--354",
    ISBN = "979-8-89176-386-9",
    abstract = "We introduce JEEM, a benchmark designed to evaluate Vision-Language Models (VLMs) on visual understanding across four Arabic-speaking countries: Jordan, The Emirates, Egypt, and Morocco. JEEM includes the tasks of image captioning and visual question answering, and features culturally rich and regionally diverse content. This dataset aims to assess the ability of VLMs to generalize across dialects and accurately interpret cultural elements in visual contexts. In an evaluation of five prominent open-source Arabic VLMs and GPT-4o, we find that the Arabic VLMs consistently underperform, struggling with both visual understanding and dialect-specific generation. While GPT-4o ranks best in this comparison, the model{'}s linguistic competence varies across dialects, and its visual understanding capabilities lag behind. This underscores the need for more inclusive models and the value of culturally-diverse evaluation paradigms."
}