AIN: The Arabic INclusive Large Multimodal Model

• URL: http://arxiv.org/abs/2502.00094v2
• Date: Tue, 04 Feb 2025 18:05:23 GMT
• Title: AIN: The Arabic INclusive Large Multimodal Model
• Authors: Ahmed Heakl, Sara Ghaboura, Omkar Thawkar, Fahad Shahbaz Khan, Hisham Cholakkal, Rao Muhammad Anwer, Salman Khan,
• Abstract summary: AIN is an English-Arabic bilingual LMM designed to excel in English and Arabic. AIN demonstrates state-of-the-art Arabic performance, while also possessing strong English-language visual capabilities. AIN's superior capabilities position it as a significant step toward empowering Arabic speakers with advanced multimodal generative AI tools.
• Score: 71.29419186696138
• License:
• 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. On the recent CAMEL-Bench benchmark comprising 38 sub-domains including, multi-image understanding, complex visual perception, handwritten document understanding, video understanding, medical imaging, plant diseases, and remote sensing-based land use understanding, our AIN demonstrates strong performance with the 7B model outperforming GPT-4o by an absolute gain of 3.4% averaged over eight domains and 38 sub-domains. AIN's superior capabilities position it as a significant step toward empowering Arabic speakers with advanced multimodal generative AI tools across diverse applications.

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