Papers by Abdellah El Mekki
Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset (2025.findings-emnlp)
Copied to clipboard
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). |
Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word Embedding (2021.naacl-main)
Copied to clipboard
| Challenge: | Recent studies have classified dialectal Arabic into more fine-grained levels, including countries and cities. |
| Approach: | They propose to use Arabic domains to transfer knowledge from labeled source domains into unlabeled target domains by transferring the learned knowledge from a labele . |
| Outcome: | The proposed method outperforms other domain adaptation methods and improves performance by 20.8% over the zero-shot transfer learning from BERT. |
Effective Self-Mining of In-Context Examples for Unsupervised Machine Translation with LLMs (2025.findings-naacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated impressive performance on a wide range of natural language processing tasks. |
| Approach: | They propose an unsupervised approach to mine in-context examples for machine translation (MT) they use word-level mining to acquire word translations that are then used to perform sentence-level mines . |
| Outcome: | The proposed approach outperforms state-of-the-art methods on 288 directions on 287 languages and is based on word-level mining and sentence-level extraction. |
Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks (2025.findings-naacl)
Copied to clipboard
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. |
NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities (2025.emnlp-main)
Copied to clipboard
| 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)
Copied to clipboard
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. |
LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing MT evaluation frameworks fail to capture dialect- and culture-specific errors in diglossic languages. |
| Approach: | They propose a hierarchical error taxonomy for diagnosing MT errors through six linguistic levels: sociolinguistics, pragmatics, semantics, morphosyntax, orthography, and graphetics. |
| Outcome: | The proposed framework produces 6,113 labeled error spans across 3,495 unique erroneous sentences . it is language-agnostic and can be easily applied to or adapted for other languages. |
Casablanca: Data and Models for Multidialectal Arabic Speech Recognition (2024.emnlp-main)
Copied to clipboard
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. |
EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing models produce outputs that are too advanced or vague for younger learners and there are no standardized benchmarks to evaluate their ability to adapt across cognitive and developmental stages. |
| Approach: | They propose to use a benchmark to assess LLMs' ability to adapt to different grade levels and to use it to evaluate their model's performance. |
| Outcome: | The proposed framework assesses the ability of large language models to adapt to grade levels across a range of subjects and grades. |