Papers by Maram Hasanain
AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs (2025.coling-main)
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Basel Mousi, Nadir Durrani, Fatema Ahmad, Md. Arid Hasan, Maram Hasanain, Tameem Kabbani, Fahim Dalvi, Shammur Absar Chowdhury, Firoj Alam
| Challenge: | a recent study has found that Arabic is underrepresented in Large Language Models, especially in dialectal variations. |
| Approach: | They propose a benchmark for Arabic Dialect and Cultural Evaluation that evaluates Arabic dialect comprehension and generation. |
| Outcome: | The proposed model outperforms multilingual models on dialect comprehension and generation, but significant challenges persist in dialect identification, generation, and translation. |
NativQA: Multilingual Culturally-Aligned Natural Query for LLMs (2025.findings-acl)
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Md. Arid Hasan, Maram Hasanain, Fatema Ahmad, Sahinur Rahman Laskar, Sunaya Upadhyay, Vrunda N Sukhadia, Mucahid Kutlu, Shammur Absar Chowdhury, Firoj Alam
| Challenge: | Existing frameworks for QA datasets lack regional specificity and cultural specificity. |
| Approach: | They propose a framework to quench native language QA datasets in native languages for LLM evaluation and tuning. |
| Outcome: | The proposed framework is scalable, language-independent and can be used to build culturally and regionally aligned QA datasets in native languages. |
LLMs for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings (2024.eacl-tutorials)
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| Challenge: | Recent advances in AI can be attributed to the remarkable performance of Large Language Models (LLMs) success of LLMs depends on specific training techniques, such as instruction tuning and prompting . |
| Approach: | They explore the capabilities of Large Language Models (LLMs) in various tasks and languages . they also examine their performance, fine-tuning, instructions tuning, and close vs. open models . |
| Outcome: | The proposed model can be used for speech and multimodal tasks across modalities, languages, and dialects. |
LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content (2025.findings-naacl)
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Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Maram Hasanain, Sahinur Rahman Laskar, Naeemul Hassan, Firoj Alam
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable success as general-purpose task solvers across various fields. |
| Approach: | They propose to develop a specialized LLM for analyzing news and social media content in a multilingual context. |
| Outcome: | The proposed model outperforms the current state-of-the-art on 23 testing sets and achieves comparable performance on 8 sets. |
LAraBench: Benchmarking Arabic AI with Large Language Models (2024.eacl-long)
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Ahmed Abdelali, Hamdy Mubarak, Shammur Chowdhury, Maram Hasanain, Basel Mousi, Sabri Boughorbel, Samir Abdaljalil, Yassine El Kheir, Daniel Izham, Fahim Dalvi, Majd Hawasly, Nizi Nazar, Youssef Elshahawy, Ahmed Ali, Nadir Durrani, Natasa Milic-Frayling, Firoj Alam
| Challenge: | Recent advances in Large Language Models (LLMs) have significantly influenced the landscape of language and speech research. |
| Approach: | They used GPT-3.5-turbo, GPT-4, BLOOMZ, Jais-13b-chat, Whisper, and USM to tackle 33 distinct tasks across 61 datasets. |
| Outcome: | The proposed model outperforms SOTA models in zero-shot learning, with a few exceptions. |
Annotating the Annotators: Analysis, Insights and Modelling from an Annotation Campaign on Persuasion Techniques Detection (2025.findings-acl)
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Davide Bassi, Dimitar Iliyanov Dimitrov, Bernardo D’Auria, Firoj Alam, Maram Hasanain, Christian Moro, Luisa Orrù, Gian Piero Turchi, Preslav Nakov, Giovanni Da San Martino
| Challenge: | Existing annotation campaigns based on heuristic guidelines have not been thoroughly discussed. |
| Approach: | They propose a probabilistic model for optimizing intervention scheduling to reduce the cost of an expert oversight in annotation tasks. |
| Outcome: | The proposed model advocates for an expert oversight in annotation tasks and periodic quality audits to reduce costs. |
LLMeBench: A Flexible Framework for Accelerating LLMs Benchmarking (2024.eacl-demo)
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Fahim Dalvi, Maram Hasanain, Sabri Boughorbel, Basel Mousi, Samir Abdaljalil, Nizi Nazar, Ahmed Abdelali, Shammur Absar Chowdhury, Hamdy Mubarak, Ahmed Ali
| Challenge: | Recent development and success of Large Language Models necessitate evaluation of their performance across diverse NLP tasks in different languages. |
| Approach: | They propose a framework that can be customized to evaluate LLMs for any NLP task, regardless of language. |
| Outcome: | The LLMeBench framework can be customized to evaluate LLMs for any NLP task, regardless of language. |
Large Language Models for Propaganda Span Annotation (2024.findings-emnlp)
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| Challenge: | Using propagandistic techniques to manipulate online audiences is increasing in recent years. |
| Approach: | They investigate whether Large Language Models (LLMs) such as GPT-4 can extract propagandistic spans and the potential of employing them to collect more cost-effective annotations. |
| Outcome: | The proposed model provides labels that have higher agreement with expert annotators and lead to specialized models that achieve state-of-the-art over an unseen Arabic testing set. |
ArMeme: Propagandistic Content in Arabic Memes (2024.emnlp-main)
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| Challenge: | a lack of media literacy is a major factor contributing to the spread of misleading information on social media. |
| Approach: | They analyze a dataset of 6K Arabic memes with manual annotations . they propose to develop computational tools for their detection . |
| Outcome: | The proposed dataset is a first resource for Arabic multimodal research. |
Can GPT-4 Identify Propaganda? Annotation and Detection of Propaganda Spans in News Articles (2024.lrec-main)
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| Challenge: | Using large language models (LLMs) to detect propaganda from text is a challenge for the development of sophisticated models. |
| Approach: | They propose to use a large propaganda dataset to identify propagandistic content in text, visual, or multimodal languages to improve their models. |
| Outcome: | The proposed model performs better on a large propaganda dataset than the existing models on skewed datasets. |
PropXplain: Can LLMs Enable Explainable Propaganda Detection? (2025.findings-emnlp)
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Maram Hasanain, Md Arid Hasan, Mohamed Bayan Kmainasi, Elisa Sartori, Ali Ezzat Shahroor, Giovanni Da San Martino, Firoj Alam
| Challenge: | Currently, propagandistic content detection studies focus on detection, with little attention given to explanations justifying the predicted label. |
| Approach: | They propose a multilingual explanation-enhanced dataset and an explanation-based LLM to address this issue. |
| Outcome: | The proposed model performs comparably while also generating explanations. |