Papers by Shady Shehata
Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset (2025.findings-emnlp)
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Fakhraddin Alwajih, Samar M. Magdy, Abdellah El Mekki, Omer Nacar, Youssef Nafea, Safaa Taher Abdelfadil, Abdulfattah Mohammed Yahya, Hamzah Luqman, Nada Almarwani, Samah Aloufi, Baraah Qawasmeh, Houdaifa Atou, Serry Sibaee, Hamzah A. Alsayadi, Walid Al-Dhabyani, Maged S. Al-shaibani, Aya El aatar, Nour Qandos, Rahaf Alhamouri, Samar Ahmad, Mohammed Anwar AL-Ghrawi, Aminetou Yacoub, Ruwa AbuHweidi, Vatimetou Mohamed Lemin, Reem Abdel-Salam, Ahlam Bashiti, Adel Ammar, Aisha Alansari, Ahmed Ashraf, Nora Alturayeif, Alcides Alcoba Inciarte, AbdelRahim A. Elmadany, Mohamedou Cheikh Tourad, Ismail Berrada, Mustafa Jarrar, Shady Shehata, Muhammad Abdul-Mageed
| Challenge: | Mainstream large vision-language models (LVLMs) inherently encode cultural biases, highlighting the need for diverse multimodal datasets. |
| Approach: | They propose to construct a large-scale Arabic multimodal dataset and benchmark explicitly designed for cultural understanding. |
| Outcome: | The proposed dataset covers ten culturally significant domains covering all Arab countries and includes two evaluation benchmarks (PEARL and PEARL-LITE) and a specialized subset (PearL-X). |
Exploring the Limitations of Detecting Machine-Generated Text (2025.coling-main)
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| Challenge: | Recent advances in the quality of the generation of text by large language models have spurred research into identifying machine-generated text. |
| Approach: | They audit classification performance for detecting machine-generated text by evaluating on texts with varying writing styles. |
| Outcome: | The proposed methods are highly sensitive to stylistic changes and complexity, and in some cases degrade entirely to random classifiers. |
JAWAHER: A Multidialectal Dataset of Arabic Proverbs for LLM Benchmarking (2025.naacl-long)
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Samar Mohamed Magdy, Sang Yun Kwon, Fakhraddin Alwajih, Safaa Taher Abdelfadil, Shady Shehata, Muhammad Abdul-Mageed
| Challenge: | Recent advances in instruction fine-tuning and alignment methods have enhanced the adaptability of large language models to user preferences. |
| Approach: | They propose a benchmark to assess LLMs’ capacity to comprehend and interpret Arabic proverbs. |
| Outcome: | The proposed model can generate accurate translations, but struggle to produce culturally nuanced and contextually relevant explanations. |
Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models (2025.findings-emnlp)
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| Challenge: | grammatical gender significantly influences image generation in text-to-image models . masculine grammatikal markers increase male representation to 73% on average . feminine grammatological markers increase female representation to 38% . |
| Approach: | They propose a cross-linguistic benchmark examining words where grammatical gender contradicts stereotypical gender associations. |
| Outcome: | The proposed benchmark examines words where grammatical gender contradicts stereotypical gender associations. |
ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic (2024.findings-acl)
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Fajri Koto, Haonan Li, Sara Shatnawi, Jad Doughman, Abdelrahman Sadallah, Aisha Alraeesi, Khalid Almubarak, Zaid Alyafeai, Neha Sengupta, Shady Shehata, Nizar Habash, Preslav Nakov, Timothy Baldwin
| Challenge: | evaluating language models in Arabic remains challenging due to limited datasets . focus has shift to reasoning and knowledge-intensive tasks due to lack of relevant datasets. |
| Approach: | They propose to use ArabicMMLU to evaluate models' understanding of Arabic . they use 40 tasks and 14,575 multiple-choice questions from school exams in different countries . |
| Outcome: | The ArabicMMLU is the first multi-task language understanding benchmark for the Arabic language . it is based on 40 tasks and 14,575 multiple-choice questions in modern standard Arabic . the models are based in different countries across North Africa, the Levant, and the Gulf regions . |
NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities (2025.emnlp-main)
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| Challenge: | Current research directions rely on synthetic data generated by translating English corpora, which often fails to represent the cultural heritage and values of local communities. |
| Approach: | They propose a method to create and retrieve pre-training data tailored to a specific community . they use Egyptian and Moroccan dialects as testbeds to test their understanding . |
| Outcome: | The proposed method outperforms existing Arabic-aware LLMs and performs on par with larger models. |
Palm: A Culturally Inclusive and Linguistically Diverse Dataset for Arabic LLMs (2025.acl-long)
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Fakhraddin Alwajih, Abdellah El Mekki, Samar Mohamed Magdy, AbdelRahim A. Elmadany, Omer Nacar, El Moatez Billah Nagoudi, Reem Abdel-Salam, Hanin Atwany, Youssef Nafea, Abdulfattah Mohammed Yahya, Rahaf Alhamouri, Hamzah A. Alsayadi, Hiba Zayed, Sara Shatnawi, Serry Sibaee, Yasir Ech-chammakhy, Walid Al-Dhabyani, Marwa Mohamed Ali, Imen Jarraya, Ahmed Oumar El-Shangiti, Aisha Alraeesi, Mohammed Anwar AL-Ghrawi, Abdulrahman S. Al-Batati, Elgizouli Mohamed, Noha Taha Elgindi, Muhammed Saeed, Houdaifa Atou, Issam Ait Yahia, Abdelhak Bouayad, Mohammed Machrouh, Amal Makouar, Dania Alkawi, Mukhtar Mohamed, Safaa Taher Abdelfadil, Amine Ziad Ounnoughene, Anfel Rouabhia, Rwaa Assi, Ahmed Sorkatti, Mohamedou Cheikh Tourad, Anis Koubaa, Ismail Berrada, Mustafa Jarrar, Shady Shehata, Muhammad Abdul-Mageed
| Challenge: | a year-long community-driven project covering all 22 Arab countries evaluates the cultural and dialectal capabilities of large language models. |
| Approach: | They propose a project to evaluate the cultural and dialectal capabilities of large language models. |
| Outcome: | The project evaluates the cultural and dialectal capabilities of several frontier LLMs. |
NurseLLM: The First Specialized Language Model for Nursing (2025.emnlp-industry)
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| Challenge: | Recent advances in large language models (LLMs) have significantly transformed medical systems, but their potential within specialized domains such as nursing remains underexplored. |
| Approach: | They propose a multi-stage data generation pipeline to build the first large scale nursing MCQ dataset to train LLMs on a broad spectrum of nursing topics. |
| Outcome: | The proposed LLM outperforms general-purpose and medical-specialized LLMs on different benchmarks, underscoring the importance of a specialized Lm for the nursing domain. |
Casablanca: Data and Models for Multidialectal Arabic Speech Recognition (2024.emnlp-main)
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Bashar Talafha, Karima Kadaoui, Samar Magdy, Mariem Habiboullah, Chafei Chafei, Ahmed El-Shangiti, Hiba Zayed, Mohamedou Tourad, Rahaf Alhamouri, Rwaa Assi, Aisha Alraeesi, Hour Mohamed, Fakhraddin Alwajih, Abdelrahman Mohamed, Abdellah El Mekki, El Moatez Billah Nagoudi, Benelhadj Saadia, Hamzah Alsayadi, Walid Al-Dhabyani, Sara Shatnawi, Yasir Ech-chammakhy, Amal Makouar, Yousra Berrachedi, Mustafa Jarrar, Shady Shehata, Ismail Berrada, Muhammad Abdul-Mageed
| Challenge: | despite recent advances in speech processing, the majority of world languages and dialects remain uncovered. |
| Approach: | They propose to collect and transcribe a new Arabic dataset for eight dialects . they also develop strong baselines exploiting the new dataset . |
| Outcome: | The proposed dataset covers eight Arabic dialects, including Algerian, Egyptian, Emirati, Jordanian, Mauritanian, Moroccan, Palestinian, and Yemeni. |
Detecting Propaganda Techniques in Code-Switched Social Media Text (2023.emnlp-main)
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| Challenge: | a new study aims to detect propaganda in multiple languages using code-switching . social media platforms have made it easier for anyone to spread information to a wide audience . |
| Approach: | They propose to detect propaganda techniques in code-switched texts using a corpus of 1,030 texts . they propose to model multilinguality directly rather than using translation . |
| Outcome: | The proposed method combines different languages within the same text, presenting a challenge for automatic systems. |