Papers by El Moatez Billah Nagoudi
Peacock: A Family of Arabic Multimodal Large Language Models and Benchmarks (2024.acl-long)
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Fakhraddin Alwajih, El Moatez Billah Nagoudi, Gagan Bhatia, Abdelrahman Mohamed, Muhammad Abdul-Mageed
| Challenge: | MLLMs have proven effective in a wide range of tasks that require complex reasoning and linguistic comprehension, but they are limited to English-based settings. |
| Approach: | They propose a family of Arabic multimodal large language models with strong vision and language capabilities. |
| Outcome: | The proposed models show strong performance on visual reasoning tasks and language capabilities. |
GPTAraEval: A Comprehensive Evaluation of ChatGPT on Arabic NLP (2023.emnlp-main)
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| Challenge: | Our study examines ChatGPT’s performance on Arabic languages and dialectal varieties. |
| Approach: | They conduct a large-scale automated and human evaluation of ChatGPT, encompassing 44 distinct language understanding and generation tasks on over 60 different datasets. |
| Outcome: | The proposed model outperforms smaller models on Arabic dialects compared to GPT-4's Modern Standard Arabic and Dialectal Arabic (DA) |
Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks (2025.findings-naacl)
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Gagan Bhatia, El Moatez Billah Nagoudi, Abdellah El Mekki, Fakhraddin Alwajih, Muhammad Abdul-Mageed
| Challenge: | In this paper, we introduce a family of embedding models addressing both small-scale and large-scale use cases. |
| Approach: | They propose to use ArabicMTEB to evaluate Arabic text embedding models . they propose to build a benchmark suite that assesses cross-lingual, multi-dialectal, multidomain, and multi-cultural Arabic text embedded models. |
| Outcome: | The proposed models outperform Multilingual-E5-large and Swan-Large in most Arabic tasks while remaining dialectally and culturally aware. |
Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19 (2021.eacl-main)
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Muhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi, Dinesh Pabbi, Kunal Verma, Rannie Lin
| Challenge: | a global pandemic of coronavirus disease 2019 has impacted millions of people . a human annotation study reveals the utility of our models on a subset of Mega-COV . |
| Approach: | They develop powerful models to analyze tweets related to the pandemic . they use a multilingual Twitter dataset with geo-location information . |
| Outcome: | The proposed model can identify whether a tweet is related to the pandemic and detect misinformation about it. |
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. |
Dolphin: A Challenging and Diverse Benchmark for Arabic NLG (2023.findings-emnlp)
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| Challenge: | Existing benchmarks for Arabic are limited, but they can be used to measure performance of different languages. |
| Approach: | They propose a benchmark for Arabic that addresses the need for a framework dedicated to Arabic languages and varieties. |
| Outcome: | The proposed benchmark covers 13 different tasks in Arabic and spans 50 test splits. |
FinTral: A Family of GPT-4 Level Multimodal Financial Large Language Models (2024.findings-acl)
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| Challenge: | FinTral is a suite of state-of-the-art multimodal large language models (LLMs) built upon the Mistral-7b model and tailored for financial analysis. |
| Approach: | They introduce FinTral, a suite of state-of-the-art multimodal large language models built upon the Mistral-7b model and tailored for financial analysis. |
| Outcome: | The proposed model outperforms ChatGPT-3.5 and GPT-4 in five out of nine tasks and surpasses GPT-4.5 in five of nine task evaluations. |
AraT5: Text-to-Text Transformers for Arabic Language Generation (2022.acl-long)
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| Challenge: | Existing models that convert text-based language problems into text-to-text format are not suitable for multilingual tasks. |
| Approach: | They propose a unified Transformer framework that converts all language problems into a text-to-text format. |
| Outcome: | The proposed model performs better on all ARGEN tasks than existing models with 49 less data. |
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. |
ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic (2021.acl-long)
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| Challenge: | Pre-trained language models (LMs) are expensive and limited in inference time . a new benchmark for multi-dialectal Arabic language understanding evaluation is developed . |
| Approach: | They introduce two powerful deep bidirectional transformer-based models, ARBERT and MARBERT . they also introduce ARLUE, a new benchmark for multi-dialectal Arabic language understanding evaluation . |
| Outcome: | The proposed models outperform monolingual models with larger vocabulary and larger datasets in Arabic language understanding evaluation. |