Papers by Muhammed Saeed
Beyond Content: How Grammatical Gender Shapes Visual Representation in Text-to-Image Models (2025.findings-emnlp)
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| Challenge: | grammatical gender significantly influences image generation in text-to-image models . masculine grammatikal markers increase male representation to 73% on average . feminine grammatological markers increase female representation to 38% . |
| Approach: | They propose a cross-linguistic benchmark examining words where grammatical gender contradicts stereotypical gender associations. |
| Outcome: | The proposed benchmark examines words where grammatical gender contradicts stereotypical gender associations. |
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. |
Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin (2024.lrec-main)
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| Challenge: | Existing datasets for Nigerian Pidgin are characterised by noise in the form of orthographic variations. |
| Approach: | They propose a phonetic-theoretic framework to generate orthographic variations to augment training data. |
| Outcome: | The proposed framework improves machine translation and sentiment analysis by combining real and synthesized orthographic variations. |