Papers by Sahinur Rahman Laskar
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. |
LlamaLens: Specialized Multilingual LLM for Analyzing News and Social Media Content (2025.findings-naacl)
Copied to clipboard
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. |
English to Hindi Multi-modal Neural Machine Translation and Hindi Image Captioning (D19-52)
Copied to clipboard
| Challenge: | Multi-modal translation is an emerging task of the MT community, where visual features of image combine with textual features of parallel source-target text to translate sentences. |
| Approach: | They propose to use convolutional neural net-works and visual geometry to extract image features and attention-based Neural MachineTranslation (NMT) system for translation. |
| Outcome: | The proposed multi-modal translation system improves translation quality and improves the quality of the captions of the images. |