@inproceedings{alwajih-etal-2024-dallah,
title = "Dallah: A Dialect-Aware Multimodal Large Language Model for {A}rabic",
author = "Alwajih, Fakhraddin and
Bhatia, Gagan and
Abdul-Mageed, Muhammad",
editor = "Habash, Nizar and
Bouamor, Houda and
Eskander, Ramy and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Abdelali, Ahmed and
Touileb, Samia and
Hamed, Injy and
Onaizan, Yaser and
Alhafni, Bashar and
Antoun, Wissam and
Khalifa, Salam and
Haddad, Hatem and
Zitouni, Imed and
AlKhamissi, Badr and
Almatham, Rawan and
Mrini, Khalil",
booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.arabicnlp-1.27",
pages = "320--336",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic
%A Alwajih, Fakhraddin
%A Bhatia, Gagan
%A Abdul-Mageed, Muhammad
%Y Habash, Nizar
%Y Bouamor, Houda
%Y Eskander, Ramy
%Y Tomeh, Nadi
%Y Abu Farha, Ibrahim
%Y Abdelali, Ahmed
%Y Touileb, Samia
%Y Hamed, Injy
%Y Onaizan, Yaser
%Y Alhafni, Bashar
%Y Antoun, Wissam
%Y Khalifa, Salam
%Y Haddad, Hatem
%Y Zitouni, Imed
%Y AlKhamissi, Badr
%Y Almatham, Rawan
%Y Mrini, Khalil
%S Proceedings of The Second Arabic Natural Language Processing Conference
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F alwajih-etal-2024-dallah
%X 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.
%U https://aclanthology.org/2024.arabicnlp-1.27
%P 320-336
Markdown (Informal)
[Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic](https://aclanthology.org/2024.arabicnlp-1.27) (Alwajih et al., ArabicNLP-WS 2024)
ACL