Dallah: A Dialect-Aware Multimodal Large Language Model for A rabic (2024)

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.

## introduction large language models (llms) have revolutionized how machines understand and generate human language. recent developments have significantly expanded the scope of these models by integrating multimodal data, enabling sophisticated interaction with both textual and visual information. despite these advances, applying nlp in linguistically diverse environments presents unique challenges, particularly in processing dialectal variations of languages and the integration of these variations within multimodal contexts. these challenges are especially pronounced in arabic, a collection of languages and varieties characterized by a rich tapestry of dialects that vary significantly across different regions. arabic dialects enrich the cultural landscape and present complex linguistic variations that standard nlp models, primarily designed for msa, often fail to solve. this linguistic diversity requires the development of specialized models that can navigate the rich world of dialectal arabic and its integration with visual data. addressing these needs is crucial for improving user interaction and preserving linguistic heritage, as many dialects are underrepresented. some may be at risk of diminishing in the face of globalization and cultural homogenization. multi-cultural and multi-modal llms @xcite @xcite are vital against cultural homogenization, where globalization tends to favour dominant languages like arabic. this can potentially lead to the marginalization or even extinction of less widely spoken dialects @xcite @xcite . by including these low-resource dialects, we can ensure their continued relevance and preserve the rich linguistic diversity of the arabicspeaking world. to this end, we introduce a powerful multimodal language model that specifically targets the unique aspects of arabic dialects. our model, dubbed dallah, is built on the foundations of llava @xcite , an advanced multimodal language model framework. we enhance llava with the linguistic capabilities of arallama @xcite , an llm proficient in arabic and english. dallah is designed to understand and generate content in arabic, navigating the complex interplay between different dialects and visual information effectively. the following are the main contributions of our work: the remainder of this paper is structured as follows: in section 2, we provide an overview of related work. section 3 details our methodology, including the processes for arabic dataset translation and filtering, construction of dialectal arabic datasets, the architecture of dallah, and the training procedures employed. in section 4, we describe our implementation details and the benchmarks used for evaluation. section 5 presents our experimental results, including both quantitative and qualitative analyses. we conclude in section 6 with a discussion of our findings and future work. ## conclusion the paper introduces dallah, an advanced multimodal large language model tailored for arabic dialects, which demonstrates superior performance in processing both standard arabic and regional dialects. developed through innovative data filtering and training, dallah achieves state-of-the-art performance in the llava-benchmark. dallah maintains dialect authenticity and content accuracy, showing promising results in benchmarks and evaluations. extensive testing shows the model's robustness in msa and across various dialects and contexts. this model marks a significant step in enhancing arabic nlp, with future goals to expand dialect coverage and refine evaluation metrics for better user interaction insights.