Papers by Abdelrahman Zayed
SafeSearch: Do Not Trade Safety for Utility in LLM Search Agents (2026.findings-eacl)
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| Challenge: | Large language model (LLM) based search agents are more likely to produce harmful outputs than base models. |
| Approach: | They propose a query-level shaping term that rewards safe queries and penalizes unsafe ones. |
| Outcome: | The proposed approach reduces harmfulness by over 70% across three red-teaming datasets while producing safe, helpful responses. |
Why Don’t Prompt-Based Fairness Metrics Correlate? (2024.acl-long)
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| Challenge: | Existing methods to assess fairness using prompts have low correlations between fairness metrics. |
| Approach: | They propose a method to enhance the correlation between fairness metrics by using pre-trained language models. |
| Outcome: | The proposed method improves the correlation between fairness metrics by using pre-trained language models. |
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. |