Papers by Shammur Absar Chowdhury
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. |
BnTTS: Few-Shot Speaker Adaptation in Low-Resource Setting (2025.findings-naacl)
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Mohammad Jahid Ibna Basher, Md Kowsher, Md Saiful Islam, Rabindra Nath Nandi, Nusrat Jahan Prottasha, Mehadi Hasan Menon, Tareq Al Muntasir, Shammur Absar Chowdhury, Firoj Alam, Niloofar Yousefi, Ozlem Garibay
| Challenge: | Empirical evaluations in few-shot settings show that BnTTS significantly improves the naturalness, intelligibility, and speaker fidelity of synthesized Bangla speech. |
| Approach: | They propose to integrate Bangla into a multilingual TTS pipeline with modifications to account for the phonetic and linguistic characteristics of the language. |
| Outcome: | The proposed framework improves the naturalness, intelligibility, and speaker fidelity of synthesized Bangla speech compared to state-of-the-art systems. |
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. |
ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination (2022.lrec-1)
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| Challenge: | Social media are integrated with our daily life and are used to circulate information. |
| Approach: | They develop and publicly release the first largest manually annotated Arabic tweet dataset for COVID-19 vaccination campaign. |
| Outcome: | The proposed dataset is the largest manually annotated Arabic tweet dataset for COVID-19 vaccination campaign, covering many countries in the Arab region. |
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. |
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus (2021.acl-long)
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| Challenge: | QASR is the largest transcribed Arabic speech corpus in the broadcast domain. |
| Approach: | They introduce the largest transcribed Arabic speech corpus, QASR, collected from the broadcast domain. |
| Outcome: | The proposed dataset contains 2,000 hours of speech sampled at 16kHz crawled from Aljazeera news channel. |
Once Correct, Still Wrong: Counterfactual Hallucination in Multilingual Vision-Language Models (2026.findings-acl)
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| Challenge: | Existing hallucination benchmarks rarely test this failure mode outside Western contexts and English. |
| Approach: | They propose a multimodal benchmark built from images spanning 17 MENA countries . they use a CFHR-based test to measure hallucination beyond raw accuracy . |
| Outcome: | The proposed model is based on images from 17 MENA countries . it measures counterfactual acceptance conditioned on correctly answering the true statement. |
A Multi-Platform Arabic News Comment Dataset for Offensive Language Detection (2020.lrec-1)
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Shammur Absar Chowdhury, Hamdy Mubarak, Ahmed Abdelali, Soon-gyo Jung, Bernard J. Jansen, Joni Salminen
| Challenge: | Social media platforms allow users to engage in conversation with limited accountability, causing hate crimes and mental harm to targeted individuals. |
| Approach: | They propose to make public a new dialectal Arabic news comment dataset . they analyze distinctive lexical content along with the use of emojis in offensive comments . |
| Outcome: | The proposed dataset analyzes offensive language and distinctive lexical content along with the use of emojis on Twitter, Facebook, and YouTube. |
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. |
RNN Simulations of Grammaticality Judgments on Long-distance Dependencies (C18-1)
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| Challenge: | LSTM networks can detect linguistic structures which are ungrammatical due to extraction violations, but are sensitive to linguistic processing factors. |
| Approach: | They propose to use LSTM networks to detect ungrammatical sentences by detecting extra arguments and subject-relative clause island violations. |
| Outcome: | The proposed model can correctly classify (un)grammatical sentences, in certain conditions, but is sensitive to linguistic processing factors and unable to induce a more abstract notion of grammaticality. |