Mustafa Ghaleb


2024

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CIDAR: Culturally Relevant Instruction Dataset For Arabic
Zaid Alyafeai | Khalid Almubarak | Ahmed Ashraf | Deema Alnuhait | Saied Alshahrani | Gubran Abdulrahman | Gamil Ahmed | Qais Gawah | Zead Saleh | Mustafa Ghaleb | Yousef Ali | Maged Al-shaibani
Findings of the Association for Computational Linguistics ACL 2024

Instruction tuning has emerged as a prominent methodology for teaching Large Language Models (LLMs) to follow instructions. However, current instruction datasets predominantly cater to English or are derived from English-dominated LLMs, leading to inherent biases toward Western culture. This bias negatively impacts non-English languages such as Arabic and the unique culture of the Arab region. This paper addresses this limitation by introducing CIDAR, the first open Arabic instruction-tuning dataset culturally aligned by native Arabic speakers. CIDAR contains 10,000 instruction and output pairs that represent the Arab region. We discuss the cultural relevance of CIDAR via the analysis and comparison to a few models fine-tuned on other datasets. Our experiments indicate that models fine-tuned on CIDAR achieve better cultural alignment compared to those fine-tuned on 30x more data.

2022

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Masader: Metadata Sourcing for Arabic Text and Speech Data Resources
Zaid Alyafeai | Maraim Masoud | Mustafa Ghaleb | Maged S. Al-shaibani
Proceedings of the Thirteenth Language Resources and Evaluation Conference

The NLP pipeline has evolved dramatically in the last few years. The first step in the pipeline is to find suitable annotated datasets to evaluate the tasks we are trying to solve. Unfortunately, most of the published datasets lack metadata annotations that describe their attributes. Not to mention, the absence of a public catalogue that indexes all the publicly available datasets related to specific regions or languages. When we consider low-resource dialectical languages, for example, this issue becomes more prominent. In this paper, we create Masader, the largest public catalogue for Arabic NLP datasets, which consists of 200 datasets annotated with 25 attributes. Furthermore, we develop a metadata annotation strategy that could be extended to other languages. We also make remarks and highlight some issues about the current status of Arabic NLP datasets and suggest recommendations to address them.