Papers by Md. Arid Hasan
AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs (2025.coling-main)
Copied to clipboard
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)
Copied to clipboard
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. |
Zero- and Few-Shot Prompting with LLMs: A Comparative Study with Fine-tuned Models for Bangla Sentiment Analysis (2024.lrec-main)
Copied to clipboard
Md. Arid Hasan, Shudipta Das, Afiyat Anjum, Firoj Alam, Anika Anjum, Avijit Sarker, Sheak Rashed Haider Noori
| Challenge: | Recent performance of Large Language Models (LLMs) in low-resource languages is under-researched due to resource constraints. |
| Approach: | They present a manually annotated dataset encompassing 33,606 Bangla tweets and Facebook comments. |
| Outcome: | The proposed model outperforms other models even in zero and few-shot scenarios. |
ArMeme: Propagandistic Content in Arabic Memes (2024.emnlp-main)
Copied to clipboard
| 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. |