Papers by Mohamed Ahmed
Exploring Multitask Learning for Low-Resource Abstractive Summarization (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work shows that training text encoders using data from multiple tasks helps to produce an encoder that can be used in numerous downstream tasks with minimal fine-tuning. |
| Approach: | They incorporate four different tasks to improve abstractive summarization performance . they use a pretrained BERT model and train all tasks using a small-scale training corpus . |
| Outcome: | The proposed model outperforms a model trained in a multitask setting with no additional summarization data. |
InfiniBench: A Benchmark for Large Multi-Modal Models in Long-Form Movies and TV Shows (2025.emnlp-main)
Copied to clipboard
Kirolos Ataallah, Eslam Mohamed Bakr, Mahmoud Ahmed, Chenhui Gou, Khushbu Pahwa, Jian Ding, Mohamed Elhoseiny
| Challenge: | Existing benchmarks fail to test the full range of cognitive skills needed to process long-form videos . |
| Approach: | They propose a benchmark to evaluate models' ability to process long-form videos rigorously. |
| Outcome: | The benchmark measures the cognitive skills of models in understanding long-form videos . it offers the largest set of question-answer pairs for long video comprehension . |
METAL: Towards Multilingual Meta-Evaluation (2024.findings-naacl)
Copied to clipboard
| Challenge: | Recent studies show that Large Language Models excel on many standard NLP benchmarks. |
| Approach: | They propose a framework for end-to-end evaluation of Large Language Models as evaluators in multilingual scenarios. |
| Outcome: | The proposed framework evaluates LLMs as evaluators in multilingual scenarios. |
Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation? (2024.findings-eacl)
Copied to clipboard
Rishav Hada, Varun Gumma, Adrian Wynter, Harshita Diddee, Mohamed Ahmed, Monojit Choudhury, Kalika Bali, Sunayana Sitaram
| Challenge: | Large Language Models (LLMs) excel in various tasks, but their evaluation, especially in languages beyond the top 20, remains inadequate due to existing benchmarks and metrics limitations. |
| Approach: | They propose to use Large Language Models as evaluators to rank or score other models’ outputs by calibrating them against 20K human judgments across three text-generation tasks, five metrics, and eight languages. |
| Outcome: | The proposed evaluation methods can be used to improve multilingual evaluation by calibrating them against 20K human judgments across three text-generation tasks, five metrics, and eight languages. |
MEGAVERSE: Benchmarking Large Language Models Across Languages, Modalities, Models and Tasks (2024.naacl-long)
Copied to clipboard
Sanchit Ahuja, Divyanshu Aggarwal, Varun Gumma, Ishaan Watts, Ashutosh Sathe, Millicent Ochieng, Rishav Hada, Prachi Jain, Mohamed Ahmed, Kalika Bali, Sunayana Sitaram
| Challenge: | Several new LLMs have been introduced necessitating their evaluation on non-English languages. |
| Approach: | They perform a thorough evaluation of the non-English capabilities of SoTA LLMs by comparing them on the same set of multilingual datasets. |
| Outcome: | The proposed model outperforms models on multilingual datasets on 22 languages including low-resource African languages. |
Multi-Dialect Arabic POS Tagging: A CRF Approach (L18-1)
Copied to clipboard
Kareem Darwish, Hamdy Mubarak, Ahmed Abdelali, Mohamed Eldesouki, Younes Samih, Randah Alharbi, Mohammed Attia, Walid Magdy, Laura Kallmeyer
| Challenge: | Existing work on dialectal POS tagging is rather scant with POS tags for most dialects being nonexistent or of limited availability. |
| Approach: | They propose a dataset of POS-tagged Arabic tweets in four major dialects and a tagging guideline for each dialect. |
| Outcome: | The proposed model can tag four different dialects with an average accuracy of 89.3%. |
FRAPPE: FRAming, Persuasion, and Propaganda Explorer (2024.eacl-demo)
Copied to clipboard
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 . |
MEGA: Multilingual Evaluation of Generative AI (2023.emnlp-main)
Copied to clipboard
Kabir Ahuja, Harshita Diddee, Rishav Hada, Millicent Ochieng, Krithika Ramesh, Prachi Jain, Akshay Nambi, Tanuja Ganu, Sameer Segal, Mohamed Ahmed, Kalika Bali, Sunayana Sitaram
| Challenge: | Large Large Models (LLMs) have shown impressive performance on many natural language processing tasks such as language understanding, reasoning, and language generation. |
| Approach: | They present a framework for evaluating generative LLMs in the multilingual setting and provide directions for future progress in the field. |
| Outcome: | The proposed framework evaluates generative models on 16 NLP datasets across 70 typologically diverse languages and compares them to state-of-the-art non-autoregressive models. |
Persuasiveness of Generated Free-Text Rationales in Subjective Decisions: A Case Study on Pairwise Argument Ranking (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Existing research on generating free-text rationales has focused on tasks where there is an expected factual ground truth. |
| Approach: | They analyze generated free-text rationales in tasks with subjective answers . they find open-source LLMs generate highly persuasive rationale models . |
| Outcome: | The proposed model outperforms closed-source models in pairwise argument ranking, a highly subjective task with potential for debate assistance. |
A System for Diacritizing Four Varieties of Arabic (D19-3)
Copied to clipboard
| Challenge: | Short vowels, aka diacritics, are omitted when writing different varieties of Arabic . diacritization is essential for language learning and text-to-speech applications . |
| Approach: | They propose a system for recovering diacritics in Arabic without short vowels . they use a character-based sequence-to-sequence deep learning model . |
| Outcome: | The proposed system beats all previous SOTA systems for Arabic varieties . it uses a character-based sequence-to-sequence deep learning model . |
KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding (2025.findings-acl)
Copied to clipboard
Ahmed Heakl, Muhammad Abdullah Sohail, Mukul Ranjan, Rania Elbadry, Ghazi Shazan Ahmad, Mohamed El-Geish, Omar Maher, Zhiqiang Shen, Fahad Shahbaz Khan, Salman Khan
| Challenge: | Optical Character Recognition (OCR) is a key component of document processing . Arabic text recognition has complex typographic and calligraphic features . |
| Approach: | They propose a comprehensive Arabic OCR benchmark that fills the gaps in evaluation systems. |
| Outcome: | The proposed benchmark outperforms existing models in Arabic by 60% in the character error rate . the best model achieves only 65% accuracy in PDF-to-Markdown conversion . |
ULMFiT replication (2020.lrec-1)
Copied to clipboard
| Challenge: | Inductive transfer learning has been well studied in Computer Vision and in Natural Language Understanding/Processing. |
| Approach: | They propose to use the knowledge gained by solving a source problem towards solving another (target) problem T t. |
| Outcome: | The problem of English text classification is motivated by practical applications like anomaly detection, security and legal applications. |
Palm: A Culturally Inclusive and Linguistically Diverse Dataset for Arabic LLMs (2025.acl-long)
Copied to clipboard
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. |
AraTrust: An Evaluation of Trustworthiness for LLMs in Arabic (2025.coling-main)
Copied to clipboard
| Challenge: | Existing benchmarks for large language models (LLMs) in Arabic are lacking . despite progress in their development, there is a lack of comprehensive trustworthiness evaluation benchmarks . |
| Approach: | They propose to use Arabic as a language to assess trustworthiness of large language models. |
| Outcome: | The proposed benchmark measures the trustworthiness of large language models in Arabic. |
Casablanca: Data and Models for Multidialectal Arabic Speech Recognition (2024.emnlp-main)
Copied to clipboard
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. |
Prompt Engineering a Prompt Engineer (2024.findings-acl)
Copied to clipboard
| Challenge: | Recent studies indicate that large language models can be meta-prompted to perform automatic prompt engineering, but their potential is limited due to insufficient guidance for complex reasoning in the meta-prompt. |
| Approach: | They propose to infuse three key components into a meta-prompt to guide reasoning . they find prompts that outperform “let’s think step by step” by 6.3% on MultiArith and 3.1% on GSM8K . |
| Outcome: | The proposed method outperforms “let’s think step by step” by 6.3% on MultiArith and 3.1% on GSM8K and outperfies baselines on counterfactual tasks by 6.9%. |
A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation (2022.naacl-main)
Copied to clipboard
David Adelani, Jesujoba Alabi, Angela Fan, Julia Kreutzer, Xiaoyu Shen, Machel Reid, Dana Ruiter, Dietrich Klakow, Peter Nabende, Ernie Chang, Tajuddeen Gwadabe, Freshia Sackey, Bonaventure F. P. Dossou, Chris Emezue, Colin Leong, Michael Beukman, Shamsuddeen Muhammad, Guyo Jarso, Oreen Yousuf, Andre Niyongabo Rubungo, Gilles Hacheme, Eric Peter Wairagala, Muhammad Umair Nasir, Benjamin Ajibade, Tunde Ajayi, Yvonne Gitau, Jade Abbott, Mohamed Ahmed, Millicent Ochieng, Anuoluwapo Aremu, Perez Ogayo, Jonathan Mukiibi, Fatoumata Ouoba Kabore, Godson Kalipe, Derguene Mbaye, Allahsera Auguste Tapo, Victoire Memdjokam Koagne, Edwin Munkoh-Buabeng, Valencia Wagner, Idris Abdulmumin, Ayodele Awokoya, Happy Buzaaba, Blessing Sibanda, Andiswa Bukula, Sam Manthalu
| Challenge: | Low-resource languages are left out of large-scale pretraining datasets . authors explore how to leverage existing pre-trained models to create low-resourced translation systems for 16 African languages. |
| Approach: | They investigate how large-scale pre-trained models can be used to create low-resource translation systems for 16 African languages. |
| Outcome: | The proposed models can translate between hundreds of languages even though there is little parallel data available for training. |
ShadowLLM: Predictor-based Contextual Sparsity for Large Language Models (2024.emnlp-main)
Copied to clipboard
Yash Akhauri, Ahmed AbouElhamayed, Jordan Dotzel, Zhiru Zhang, Alexander Rush, Safeen Huda, Mohamed Abdelfattah
| Challenge: | Prior work has focused on contextual sparsity, but it has not been successful. |
| Approach: | They propose a novel pruning predictor that can shadow the LLM behavior and enforce better sparsity patterns. |
| Outcome: | The proposed model can shadow the LLM behavior and enforce better sparsity patterns, resulting in 15% improvement in end-to-end accuracy compared to prior methods. |