Papers by Md Fahim
Evaluating Large Vision Language Models on Bangla Medical Visual Question Answering (2026.findings-acl)
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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. |
BANMIME : Misogyny Detection with Metaphor Explanation on Bangla Memes (2025.emnlp-main)
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Md Ayon Mia, Akm Moshiur Rahman Mazumder, Khadiza Sultana Sayma, Md Fahim, Md Tahmid Hasan Fuad, Muhammad Ibrahim Khan, Akmmahbubur Rahman
| Challenge: | Existing studies have explored hate speech and general meme classification, but the nuanced identification of misogyny in Bangla memes remains underexplored. |
| Approach: | They propose a Bangla misogynistic meme dataset that includes misos, humor, metaphors and detailed human-written explanations. |
| Outcome: | The proposed dataset is the first comprehensive dataset of misogynistic Bangla memes . it includes misos, humor categories, metaphor localization, and detailed human-written explanations based on 2,000 culturally grounded samples . |
BanglaTLit: A Benchmark Dataset for Back-Transliteration of Romanized Bangla (2024.findings-emnlp)
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| Challenge: | low-resource languages like Bangla are limited by the lack of datasets. |
| Approach: | They propose a large-scale transliteration dataset and a pre-training corpus on romanized Bangla. |
| Outcome: | The proposed datasets show that the proposed methods can enrich romanized Bangla. |
DM-Codec: Distilling Multimodal Representations for Speech Tokenization (2025.findings-emnlp)
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Md Mubtasim Ahasan, Md Fahim, Tasnim Mohiuddin, Akmmahbubur Rahman, Aman Chadha, Tariq Iqbal, M Ashraful Amin, Md Mofijul Islam, Amin Ahsan Ali
| Challenge: | Existing speech tokenization models lack contextual representations for speech synthesis . absence of contextual representation results in elevated WER and WIL scores . |
| Approach: | They propose a language model-guided distillation method that incorporates contextual information into a comprehensive speech tokenizer. |
| Outcome: | The proposed method outperforms state-of-the-art tokenization models in reducing WER and WIL scores. |
BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Bangla (2025.findings-naacl)
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Fabiha Haider, Fariha Tanjim Shifat, Md Farhan Ishmam, Md Sakib Ul Rahman Sourove, Deeparghya Dutta Barua, Md Fahim, Md Farhad Alam Bhuiyan
| Challenge: | Existing work on monolingual or binary hate classification in Bangla has not addressed the challenge of multi-label hate speech classification in underrepresented languages. |
| Approach: | They propose a multi-label transliterated Bangla hate speech dataset that translates or transliterates under-resourced text to higher-resource text before classifying the hate group(s). |
| Outcome: | The proposed approach outperforms other methods in the zero-shot setting while achieving state-of-the-art performance. |
BanHADEX: Towards Explainable HAte Speech Detection in Bangla Using Human Annotated EXplanation (2026.acl-long)
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Faisal Hossain Raquib, Akm Moshiur Rahman Mazumder, Md Fahim, Md Tahmid Hasan Fuad, Md Farhan Ishmam, Faria Sultana, M Ashraful Amin, Amin Ahsan Ali, Akmmahbubur Rahman
| Challenge: | Existing studies in Bangla focus on hate classification while overlooking interpretability. |
| Approach: | They propose to create a dataset with human-annotated labels for banla that contains 19,203 YouTube comments spanning April 2024–June 2025. |
| Outcome: | The proposed dataset outperforms existing datasets on open and closed-source LLMs on interpretability and better understanding of hate speech in linguistically rich yet under-resourced languages. |