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arXiv · 2503.21910

JEEM: Vision-Language Understanding in Four Arabic Dialects

Karima Kadaoui·Hanin Atwany·Hamdan Al-Ali·Abdelrahman Mohamed·Ali Mekky·Sergei Tilga·Natalia Fedorova·Ekaterina Artemova·Hanan Aldarmaki·Yova Kementchedjhieva
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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-4V, we find that the Arabic VLMs consistently underperform, struggling with both visual understanding and dialect-specific generation. While GPT-4V 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.

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Karima Kadaoui, Hanin Atwany, Hamdan Al-Ali, Abdelrahman Mohamed, Ali Mekky, Sergei Tilga, Natalia Fedorova, Ekaterina Artemova, Hanan Aldarmaki, Yova Kementchedjhieva. 2025-03-27. JEEM: Vision-Language Understanding in Four Arabic Dialects. https://arxiv.org/abs/2503.21910

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