Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic
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.
66a3c1fd4366ebab2c8a0291
Fakhraddin Alwajih
66a3c1fd4366ebab2c8a0292
Gagan Bhatia
claimed_verified
2024-07-29T07:05:49.422Z
60394599033b61166496163b
https://cdn-avatars.huggingface.co/v1/production/uploads/1614366097007-noauth.jpeg
Gagan Bhatia
user
gagan3012
66a3c1fd4366ebab2c8a0293
Muhammad Abdul-Mageed
Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic
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.
An Arabic multimodal assistant named Dallah, based on LLaMA-2, achieves top performance in multimodal interactions, including handling dialectal Arabic, through fine-tuning.
Multimodal Large Language Models MLLMs Dallah LLaMA-2 multimodal interactions Arabic dialects Modern Standard Arabic MSA dialect-aware Arabic MLLMs