Papers by Omar El Herraoui
MixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts Models (2026.acl-demo)
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| Challenge: | MixtureKit is a modular open-source framework for constructing, training, and analyzing Mixture-of-Experts (MoE) models from arbitrary pre-trained or fine-tuned checkpoints. |
| Approach: | They propose a modular open-source framework for constructing, training, and analyzing Mixture-of-Experts (MoE) models from arbitrary pre-trained or fine-tuned checkpoints. |
| Outcome: | Experiments on multilingual code-switched (Arabic–Latin) show that BTX models built with MixtureKit outperform dense baselines across multiple benchmarks. |
FRAPPE: FRAming, Persuasion, and Propaganda Explorer (2024.eacl-demo)
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Ahmed Sajwani, Alaa El Setohy, Ali Mekky, Diana Turmakhan, Lara Hassan, Mohamed El Zeftawy, Omar El Herraoui, Osama Afzal, Qisheng Liao, Tarek Mahmoud
| Challenge: | FRAPPE is a linguistic analysis, persuasion, and propaganda-based news analysis system that analyzes articles for genre, framings, and persulasion techniques. |
| Approach: | They propose a FRAming, Persuasion, and Propaganda Explorer system that analyzes articles for genre, framings, and use of persuation techniques. |
| Outcome: | FRAPPE analyzes articles for genre, framings, and use of persuasion techniques . it also draws comparisons between persulasion and framping strategies adopted by a diverse pool of news outlets and countries across multiple languages for different topics . |
Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues (2026.acl-long)
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Muhammad Dehan Al Kautsar, Saeed Almheiri, Momina Ahsan, Bilal Elbouardi, Younes Samih, Sarfraz Ahmad, Amr Keleg, Omar El Herraoui, Kareem Elzeky, Abed Alhakim Freihat, Mohamed Anwar, Zhuohan Xie, Junhong Liang, Mohammad Rustom Al Nasar, Preslav Nakov, Fajri Koto
| Challenge: | Most benchmarks focus on short text snippets in Modern Standard Arabic (MSA), overlooking cultural nuances that naturally arise in dialogues. |
| Approach: | They propose a culturally grounded conversational dataset covering 13 Arabic-speaking countries, in both Modern Standard Arabic (MSA) and each country’s respective dialect, spanning 12 daily-life topics and 54 fine-grained subtopics. |
| Outcome: | The proposed model performs worse on all three tasks than the MSA benchmark. |