metadata

language:
  - ar
pipeline_tag: visual-question-answering

Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic

Dallah is an advanced multimodal large language model (MLLM) tailored for the Arabic language, with a specific focus on understanding and generating content across various Arabic dialects. Built upon the LLaVA framework and powered by the LLaMA-2 architecture, Dallah integrates both textual and visual data to facilitate comprehensive multimodal interactions.

Model Details

Architecture: LLaVA-based multimodal model with LLaMA-2 backbone.
Languages Supported: Modern Standard Arabic (MSA) and six major Arabic dialects.
Modalities: Text and image.

Training Data

Dallah was fine-tuned on a diverse dataset encompassing both textual and visual information:

Textual Data: Includes MSA and six prominent Arabic dialects, ensuring the model's proficiency across different regional linguistic variations.
Visual Data: Comprised of image-text pairs, enabling the model to process and generate content that integrates both modalities.

Performance

Dallah demonstrates state-of-the-art performance in Arabic MLLMs:

Excels in both MSA and dialectal Arabic benchmarks.
Effectively handles complex multimodal interactions involving textual and visual elements.

Applications

Dallah’s multimodal and dialect-aware capabilities make it suitable for a range of applications, including:

Multilingual Chatbots: Enhancing user interactions by understanding and responding in specific Arabic dialects.
Content Creation: Assisting in generating culturally and linguistically appropriate content for diverse Arabic-speaking audiences.
Educational Tools: Supporting language learning by providing examples and explanations in various dialects.
Cultural Preservation: Documenting and promoting the use of different Arabic dialects on digital platforms.

Citation

If you use Dallah in your research or applications, please cite the following paper:

@inproceedings{alwajih2024dallah,
  title={Dallah: A Dialect-Aware Multimodal Large Language Model for Arabic},
  author={Alwajih, Fakhraddin and Bhatia, Gagan and Abdul-Mageed, Muhammad},
  booktitle={Proceedings of The Second Arabic Natural Language Processing Conference},
  pages={320--336},
  year={2024},
  address={Bangkok, Thailand},
  publisher={Association for Computational Linguistics},
  url={https://aclanthology.org/2024.arabicnlp-1.27}
}