@inproceedings{dahimi-etal-2026-foundation,
    title = "How Foundation Models Behave for {A}rabic Image Captioning?",
    author = "Dahimi, Khaoula  and
      Belabbaci, Amel  and
      Cherroun, Hadda  and
      Haouhat, Abdelhamid",
    editor = "Al-Khalifa, Hend  and
      El-Haj, Mo  and
      Ezzini, Saad",
    booktitle = "The 7th Workshop on Open-Source {A}rabic Corpora and Processing Tools ({OSACT}7) with 5 Shared Tasks",
    month = may,
    year = "2026",
    address = "Palma, Mallorca (Spain)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/revision-workflow/2026.osact-1.6/",
    doi = "10.63317/3bhwcpon3fv5",
    pages = "49--58",
    abstract = "Image captioning plays a crucial role in numerous applications, including educational systems. However, ensuring caption quality remains a significant challenge, particularly for morphologically rich, low-resource languages such as Arabic. We investigate an evaluation of Arabic image captioning using state-of-the-art multimodal foundation models. We systematically assess the performance of leading models{---}Gemini, Gemma, LLaMA, and Fanar. Our evaluation framework employs a diverse set of metrics spanning rule-based, learnable, visually-grounded, and LLM-based approaches to capture semantic accuracy, linguistic fluency, and hallucination detection. Experiments are conducted on two benchmark datasets: Flickr8k-Arabic and JEEM. Our findings reveal significant performance variations across models and evaluation metrics, highlighting the need for Arabic-specific optimization in multimodal architectures."
}