title: AIN: The Arabic INclusive Large Multimodal Model
summary: 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.
paper_authors._id: 67a185ab908f4534beb94b8c
paper_authors.name: Ahmed Heakl
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-04T09:39:32.712Z
paper_authors._id: 656864e12d73834278a8dea7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/656864e12d73834278a8dea7/sfAWS2eyPtFHb_2GZIypp.jpeg
paper_authors.fullname: Ahmed Heakl
paper_authors.type: user
paper_authors.user: ahmedheakl
paper_authors._id: 67a185ab908f4534beb94b8d
paper_authors.name: Sara Ghaboura
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-06T16:06:32.048Z
paper_authors._id: 66d559103c5bc37ee0dfa61b
paper_authors.avatarUrl: /avatars/8310fc0b01e6d8873aec37ba9ef27c5b.svg
paper_authors.fullname: SaraG
paper_authors.type: user
paper_authors.user: SLMLAH
paper_authors._id: 67a185ab908f4534beb94b8e
paper_authors.name: Omkar Thawkar
paper_authors._id: 67a185ab908f4534beb94b8f
paper_authors.name: Fahad Shahbaz Khan
paper_authors._id: 67a185ab908f4534beb94b90
paper_authors.name: Hisham Cholakkal
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-04T17:36:17.368Z
paper_authors._id: 654a5f4f9b8bd6406d45bb46
paper_authors.avatarUrl: /avatars/ac0d7eef62cd98a280b162cf7896b1a2.svg
paper_authors.fullname: Hisham Cholakkal
paper_authors.type: user
paper_authors.user: hishamcholakkal
paper_authors._id: 67a185ab908f4534beb94b91
paper_authors.name: Rao Muhammad Anwer
paper_authors._id: 67a185ab908f4534beb94b92
paper_authors.name: Salman Khan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-04T17:36:30.190Z
paper_authors._id: 65337cfbbadc49780755d1d1
paper_authors.avatarUrl: /avatars/527f456a6a95e0b3a143be130b9b9258.svg
paper_authors.fullname: Salman Khan
paper_authors.type: user
paper_authors.user: SalmanKhan
paper_title: AIN: The Arabic INclusive Large Multimodal Model
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) large multimodal models (LMMs) multimodal data 3.6 million high-quality Arabic-English multimodal data samples CAMEL-Bench benchmark multi-image understanding complex visual perception handwritten document understanding video understanding medical imaging plant diseases remote sensing-based land use understanding bilingual LMM multimodal generative AI tools