AIN: The Arabic INclusive Large Multimodal Model
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.
67a185ab908f4534beb94b8c
Ahmed Heakl
claimed_verified
2025-02-04T09:39:32.712Z
656864e12d73834278a8dea7
https://cdn-avatars.huggingface.co/v1/production/uploads/656864e12d73834278a8dea7/sfAWS2eyPtFHb_2GZIypp.jpeg
Ahmed Heakl
user
ahmedheakl
67a185ab908f4534beb94b8d
Sara Ghaboura
claimed_verified
2025-02-06T16:06:32.048Z
66d559103c5bc37ee0dfa61b
/avatars/8310fc0b01e6d8873aec37ba9ef27c5b.svg
SaraG
user
SLMLAH
67a185ab908f4534beb94b8e
Omkar Thawkar
67a185ab908f4534beb94b8f
Fahad Shahbaz Khan
67a185ab908f4534beb94b90
Hisham Cholakkal
admin_assigned
2025-02-04T17:36:17.368Z
654a5f4f9b8bd6406d45bb46
/avatars/ac0d7eef62cd98a280b162cf7896b1a2.svg
Hisham Cholakkal
user
hishamcholakkal
67a185ab908f4534beb94b91
Rao Muhammad Anwer
67a185ab908f4534beb94b92
Salman Khan
admin_assigned
2025-02-04T17:36:30.190Z
65337cfbbadc49780755d1d1
/avatars/527f456a6a95e0b3a143be130b9b9258.svg
Salman Khan
user
SalmanKhan
AIN: The Arabic INclusive Large Multimodal Model
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.
AIN, a bilingual English-Arabic multimodal model, achieves state-of-the-art performance in Arabic and strong English-language visual capabilities across diverse domains.
large language models large multimodal models multilingual models bilingual models multimodal data CAMEL-Bench sub-domains multi-image understanding complex visual perception handwritten document understanding video understanding medical imaging plant diseases remote sensing-based land use understanding generative AI