title: Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic
summary: Recent advancements have significantly enhanced the capabilities of
  Multimodal Large Language Models (MLLMs) in generating and understanding
  image-to-text content. Despite these successes, progress is predominantly
  limited to English due to the scarcity of high quality multimodal resources in
  other languages. This limitation impedes the development of competitive models
  in languages such as Arabic. To alleviate this situation, we introduce an
  efficient Arabic multimodal assistant, dubbed Dallah, that utilizes an advanced
  language model based on LLaMA-2 to facilitate multimodal interactions. Dallah
  demonstrates state-of-the-art performance in Arabic MLLMs. Through fine-tuning
  six Arabic dialects, Dallah showcases its capability to handle complex
  dialectal interactions incorporating both textual and visual elements. The
  model excels in two benchmark tests: one evaluating its performance on Modern
  Standard Arabic (MSA) and another specifically designed to assess dialectal
  responses. Beyond its robust performance in multimodal interaction tasks,
  Dallah has the potential to pave the way for further development of
  dialect-aware Arabic MLLMs.
paper_authors._id: 66a3c1fd4366ebab2c8a0291
paper_authors.name: Fakhraddin Alwajih
paper_authors._id: 66a3c1fd4366ebab2c8a0292
paper_authors.name: Gagan Bhatia
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-07-29T07:05:49.422Z
paper_authors._id: 60394599033b61166496163b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1614366097007-noauth.jpeg
paper_authors.fullname: Gagan Bhatia
paper_authors.type: user
paper_authors.user: gagan3012
paper_authors._id: 66a3c1fd4366ebab2c8a0293
paper_authors.name: Muhammad Abdul-Mageed
paper_title: Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic
paper_summary: Recent advancements have significantly enhanced the capabilities of
  Multimodal Large Language Models (MLLMs) in generating and understanding
  image-to-text content. Despite these successes, progress is predominantly
  limited to English due to the scarcity of high quality multimodal resources in
  other languages. This limitation impedes the development of competitive models
  in languages such as Arabic. To alleviate this situation, we introduce an
  efficient Arabic multimodal assistant, dubbed Dallah, that utilizes an advanced
  language model based on LLaMA-2 to facilitate multimodal interactions. Dallah
  demonstrates state-of-the-art performance in Arabic MLLMs. Through fine-tuning
  six Arabic dialects, Dallah showcases its capability to handle complex
  dialectal interactions incorporating both textual and visual elements. The
  model excels in two benchmark tests: one evaluating its performance on Modern
  Standard Arabic (MSA) and another specifically designed to assess dialectal
  responses. Beyond its robust performance in multimodal interaction tasks,
  Dallah has the potential to pave the way for further development of
  dialect-aware Arabic MLLMs.
paper_ai_summary: An Arabic multimodal assistant named Dallah, based on LLaMA-2, achieves top performance in multimodal interactions, including handling dialectal Arabic, through fine-tuning.
paper_ai_keywords: Multimodal Large Language Models MLLMs Dallah LLaMA-2 multimodal interactions Arabic dialects Modern Standard Arabic MSA dialect-aware Arabic MLLMs