--- license: mit language: - en - ar base_model: - qwen2-VL-7B pipeline_tag: image-text-to-text tags: - LMM - Arabic ---
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AIN: The Arabic INclusive Large Multimodal Model

[Ahmed Heakl](https://huggingface.co/ahmedheakl) *   [Sara Ghaboura](https://huggingface.co/SLMLAH) *   [Omkar Thawakar](https://omkarthawakar.github.io)   [Fahad Shahbaz Khan](https://scholar.google.com/citations?hl=en&user=zvaeYnUAAAAJ)   [Hisham Cholakkal](https://scholar.google.com/citations?hl=en&user=bZ3YBRcAAAAJ)   [Rao M. Anwer](https://scholar.google.com/citations?hl=en&user=_KlvMVoAAAAJ)   [Salman Khan](https://scholar.google.com/citations?hl=en&user=M59O9lkAAAAJ)
*Equal Contribution
#### **Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), UAE** [![arXiv](https://img.shields.io/badge/arXiv-2502.00094-3399FF)](https://arxiv.org/abs/2502.00094) [![Our Page](https://img.shields.io/badge/Visit-Our%20Page-8C7AFF?style=flat)](https://mbzuai-oryx.github.io/AIN/) [![GitHub issues](https://img.shields.io/github/issues/mbzuai-oryx/Camel-Bench?color=FFF359&label=issues&style=flat)](https://github.com/mbzuai-oryx/AIN/issues) [![GitHub stars](https://img.shields.io/github/stars/mbzuai-oryx/AIN?color=FF6A07&style=flat)](https://github.com/mbzuai-oryx/AIN/stargazers) [![GitHub license](https://img.shields.io/github/license/mbzuai-oryx/Camel-Bench?color=FF6666)](https://github.com/mbzuai-oryx/AIN/blob/main/LICENSE) --- https://github.com/mbzuai-oryx/AIN

Abstract

Amid the swift progress of large language models (LLMs) and their evolution into large multimodal models (LMMs), significant strides have been made in high-resource languages such as English and Chinese. While Arabic LLMs have seen notable progress, Arabic LMMs remain largely unexplored, often narrowly focusing on a few specific aspects of the language and visual understanding. To bridge this gap, we introduce AIN - the Arabic Inclusive Multimodal Model- designed to excel across diverse domains. AIN is an English-Arabic bilingual LMM designed to excel in English and Arabic, leveraging carefully constructed 3.6 million high-quality Arabic-English multimodal data samples. AIN demonstrates state-of-the-art Arabic performance, while also possessing strong English-language visual capabilities.

## 🌟 Key Features - The **first Arabic-centric inclusive Large Multimodal Model (LMM)** trained on **3.6M samples**. - Includes **35% authentic Arabic data** within its Arabic data subset. - Achieves **superior performance compared to open- and closed-source models** (e.g., GPT-4o) and open-source models (e.g., Qwen2-VL-7B) across tasks such as OCR and specialized domains. - Demonstrates **robust bilingual capabilities** (Arabic/English), **validated** through **comprehensive testing** and **human evaluation** across 17 Arab countries. - Exhibits **advanced cultural understanding** and domain expertise in fields such as **medical imaging**, **agriculture**, and **scientific visualization**.

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Figure 1. Comparative performance of AIN-7B against other models across key domains, including OCR & Document Understanding, Remote Sensing, Agricultural Understanding, and overall performance across all domains.

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Figure 2. showcases a comprehensive performance analysis of AIN-7B across CAMEL-Bench domains, comparing it with prominent closed-source models as well as open-source counterparts. OCR: "OCR & Document Understanding", Video: "General Video & Multi-Image Understanding", RS: "Remote Sensing Understanding", CDT: "Chart, Diagram & Table Understanding", Agro.: "Agricultural Image Understanding", Cultural: "Cultural-Specific Understanding", Medical: "Medical Image Understanding".
--- ## βš–οΈ Quantitative Evaluation and Results AIN demonstrates state-of-the-art performance across diverse domains, surpassing both open- and closed-source models. Notably, it achieves an aggregate performance score of 63.77%, with significant gains in OCR, remote sensing, and agricultural image understanding.
Table 1. Performance comparison of AIN and different closed- and open-source LMMs across CAMEL-Bench domains.
Best performance is marked with πŸ₯‡; second-best is πŸ₯ˆ. OCR: "OCR & Document Understanding", Video: "General Video & Multi-Image Understanding", RS: "Remote Sensing Understanding", CDT: "Chart, Diagram & Table Understanding", Agro.: "Agricultural Image Understanding", Cult.: "Cultural-Specific Understanding", Med.: "Medical Image Understanding".
Models VQA OCR Video RS CDT Agro. Cult. Med. Total
GPT-4o πŸ₯ˆ55.15 πŸ₯ˆ54.98 πŸ₯‡69.65 πŸ₯ˆ27.36 πŸ₯ˆ62.35 πŸ₯ˆ80.75 πŸ₯‡80.86 πŸ₯‡49.91 πŸ₯ˆ60.13
GPT-4o-mini 48.83 39.38 πŸ₯ˆ66.28 16.93 56.37 78.80 65.92 πŸ₯ˆ47.37 52.49
Gemini-1.5-Pro 46.68 28.68 42.95 17.07 47.06 72.14 56.24 33.78 52.38
Gemini-1.5-flash 45.59 27.58 53.31 14.95 48.26 76.07 46.54 42.87 44.40
InternVL-8B 30.41 15.91 51.42 5.36 30.27 44.47 20.88 29.48 28.52
InternVL2.5-1B 27.22 19.45 38.20 3.39 30.75 39.53 35.68 21.27 26.94
Qwen-VL-2B 41.02 22.93 38.90 12.56 27.83 52.02 34.28 29.12 32.33
AIN-7B (ours) πŸ₯‡56.78 πŸ₯‡72.35 64.09 πŸ₯‡45.92 πŸ₯‡64.10 πŸ₯‡85.05 πŸ₯ˆ78.09 43.77 πŸ†63.77
--- ## 🎯 Qualitative Evaluation The qualitative evaluation showcases AIN's advanced capabilities in handling diverse, complex tasks, including OCR, medical imaging, remote sensing, and cultural-specific understanding, with remarkable precision and contextual relevance. Unlike GPT-4o and LLaVA, AIN demonstrates superior performance in identifying intricate details and maintaining accuracy across varied query formats and multi-domain challenges.
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Figure 3. Qualitative examples showcasing AIN-7B’s capabilities across various domains, including general VQA, OCR & Document Understanding, Remote Sensing, Medical Imaging, Agricultural Understanding, and Cultural-Specific tasks.
--- ## 🧐 Data Verification and Toxicity Filtering A multi-step verification pipeline was implemented to ensure high-quality translations and safe visual data. Translation accuracy was assessed through human evaluation, where native Arabic speakers rated outputs against reference translations, and semantic similarity checks were conducted using **LaBSE**. Additionally, translated samples were reverse-translated and validated using **BLEU, METEOR, and ROUGE scores** to measure correctness, correlation, and overlap. For visual data, toxicity filtering was applied using **LLavaGuard’s safety policies and GPT-4o**, identifying and removing unsafe content related to violence, substance abuse, and harmful imagery, ensuring compliance with ethical AI standards.

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Figure 4. Data verification and filtering pipeline for textual and visual data, ensuring high-quality training data through semantic similarity checks, translation quality evaluations, and toxicity screening for safety compliance.

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Figure 5. Distribution of visual data toxicity filtering results, showing that 95% of the data is classified as safe, while 5% is identified as unsafe due to categories like weapons or substance abuse, violence, and animal cruelty.

--- ## πŸ”’ License This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. ## πŸ’¬ Contact us For questions or suggestions, feel free to reach out to us on [GitHub Discussions](https://github.com/mbzuai-oryx/AIN/discussions). --- If you use AIN in your research, please cite our work as follows: ``` @misc{heakl2025ainarabicinclusivelarge, title={AIN: The Arabic INclusive Large Multimodal Model}, author={Ahmed Heakl and Sara Ghaboura and Omkar Thawkar and Fahad Shahbaz Khan and Hisham Cholakkal and Rao Muhammad Anwer and Salman Khan}, year={2025}, eprint={2502.00094}, url={https://arxiv.org/abs/2502.00094}, ```