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
Published: 2025-01-31 18:58:20

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.