Papers by Mohammed Saeed
Transformers for Tabular Data Representation: A Survey of Models and Applications (2023.tacl-1)
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| Challenge: | Recent research efforts extend LMs by developing neural representations for structured data. |
| Approach: | They propose to extend transformer-based language models to tabular data by analyzing inputs, model training, and supported downstream tasks. |
| Outcome: | The proposed models are compared against existing models and are based on a traditional pipeline. |
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. |
RuleBERT: Teaching Soft Rules to Pre-Trained Language Models (2021.emnlp-main)
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| Challenge: | Pre-trained language models (PLMs) are limited in their ability to capture and use common-sense knowledge. |
| Approach: | They propose to teach PLMs how to reason with soft Horn rules by leveraging logical rules to learn how to predict precise probabilities. |
| Outcome: | The proposed model performs well on logical rules that were unseen at training. |
You Are My Type! Type Embeddings for Pre-trained Language Models (2022.findings-emnlp)
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| Challenge: | Existing work has shown that Pre-trained language models can encode semantic types, but it is not clear how to use types to steer the output. |
| Approach: | They propose to embed a type by a small set of word examples to promote desired types in a PLM. |
| Outcome: | The proposed model can represent types and steer masking predictions without changes to the prompt text without changes in the prompt. |