Papers by Md Hossain
Evaluating Large Vision Language Models on Bangla Medical Visual Question Answering (2026.findings-acl)
Copied to clipboard
Rafid Ahmed, Intesar Tahmid, Mir Sazzat Hossain, Tasnimul Hossain Tomal, Md Mahir Jawad, Anam Borhan Uddin, Md Fahim, Md Farhad Alam Bhuiyan
| Challenge: | Recent advances in Large Language Models and Large Vision Language Model (LVLMs) have demonstrated promising capabilities in complex reasoning tasks, but low-resource contexts like Bangla are underexplored. |
| Approach: | They propose a multilingual medical visual question answering dataset using Bangla. |
| Outcome: | The proposed model performs well on generalized visual tasks but struggles with fine-grained diagnostic reasoning, achieving low accuracy in specialized categories. |
BenNumEval: A Benchmark to Assess LLMs’ Numerical Reasoning Capabilities in Bengali (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) excel in general-purpose tasks but struggle with numerical reasoning, especially in low-resource languages like Bengali. |
| Approach: | They propose a benchmark to assess LLMs on numerical reasoning tasks in Bengali. |
| Outcome: | The proposed benchmark assesses LLMs on numerical reasoning tasks in Bengali. |
Interpreting Indirect Answers to Yes-No Questions in Multiple Languages (2023.findings-emnlp)
Copied to clipboard
Zijie Wang, Md Hossain, Shivam Mathur, Terry Melo, Kadir Ozler, Keun Park, Jacob Quintero, MohammadHossein Rezaei, Shreya Shakya, Md Uddin, Eduardo Blanco
| Challenge: | Existing models for Yes-no questions skip polar keywords and instead use long explanations that must be interpreted. |
| Approach: | They propose a distant supervision approach to collect training data and show that direct answers are useful to train models to interpret indirect answers. |
| Outcome: | The proposed model achieves a 68% to 76% F1-score on multilingual Question-Answering benchmarks. |