activity
20242026
collaborators

10 papers

cs.CL2026

LQM: Linguistically Motivated Multidimensional Quality Metrics for Machine Translation

Samar M. Magdy, Fakhraddin Alwajih, Abdellah El Mekki +2

Existing MT evaluation frameworks, including automatic metrics and human evaluation schemes such as Multidimensional Quality Metrics (MQM), are largely language-agnostic. However,…

cs.CL2026

Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs

Abdellah El Mekki, Samar M. Magdy, Houdaifa Atou +44

Arabic is a highly diglossic language where most daily communication occurs in regional dialects rather than Modern Standard Arabic (MSA). Despite this, machine translation (MT) sy…

cs.CL2025

EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs

Numaan Naeem, Abdellah El Mekki, Muhammad Abdul-Mageed

Large language models (LLMs) are transforming education by answering questions, explaining complex concepts, and generating content across a wide range of subjects. Despite strong…

cs.CL2025

Pearl: A Multimodal Culturally-Aware Arabic Instruction Dataset

Fakhraddin Alwajih, Samar M. Magdy, Abdellah El Mekki +34

Mainstream large vision-language models (LVLMs) inherently encode cultural biases, highlighting the need for diverse multimodal datasets. To address this gap, we introduce PEARL, a…

cs.CL2025

NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities

Abdellah El Mekki, Houdaifa Atou, Omer Nacar +2

Enhancing the linguistic capabilities of Large Language Models (LLMs) to include low-resource languages is a critical research area. Current research directions predominantly rely…

cs.CL2025

PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture

Fakhraddin Alwajih, Abdellah El Mekki, Hamdy Mubarak +3

Large Language Models (LLMs) inherently reflect the vast data distributions they encounter during their pre-training phase. As this data is predominantly sourced from the web, ther…