AIN: The Arabic INclusive Large Multimodal Model
Authors: name: Ahmed Heakl, name: Sara Ghaboura, name: Omkar Thawkar, name: Fahad Shahbaz Khan, name: Hisham Cholakkal, name: Rao Muhammad Anwer, name: Salman Khan
Organization: Mohamed Bin Zayed University of Artificial Intelligence

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.