Papers by Swakkhar Shatabda
Ready to Translate, Not to Represent? Bias and Performance Gaps in Multilingual LLMs Across Language Families and Domains (2026.findings-acl)
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Md. Faiyaz Abdullah Sayeedi, Subhey Sadi Rahman, Md. Mahbub Alam, Md. Adnanul Islam, Jannatul Ferdous Deepti, Tasnim Mohiuddin, Md Mofijul Islam, Swakkhar Shatabda
| Challenge: | Large Language Models (LLMs) have redefined Machine Translation, enabling context-aware and fluent translations across hundreds of languages and textual domains. |
| Approach: | They propose a framework and dataset to evaluate the translation quality and fairness of open-source LLMs. |
| Outcome: | The proposed framework and dataset evaluates translation quality and fairness of open-source LLMs. |
MathMist: A Parallel Multilingual Benchmark Dataset for Mathematical Problem Solving and Reasoning (2026.findings-eacl)
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Mahbub E Sobhani, Md. Faiyaz Abdullah Sayeedi, Tasnim Mohiuddin, Md Mofijul Islam, Swakkhar Shatabda
| Challenge: | Existing benchmarks primarily focus on English or a narrow subset of high-resource languages, leaving significant gaps in assessing multilingual and cross-lingual mathematical reasoning. |
| Approach: | They propose a parallel multilingual benchmark for mathematical problem solving and reasoning that encompasses 2,890 parallel Bangla-English gold standard artifacts. |
| Outcome: | The proposed model encompasses 2,890 parallel Bangla-English gold standard artifacts, totaling 30K aligned question–answer pairs across thirteen languages, representing high-, medium-, and low-resource linguistic settings. |
BnMMLU: Measuring Massive Multitask Language Understanding in Bengali (2026.findings-acl)
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| Challenge: | Large-scale multitask benchmarks have driven rapid progress in language modeling, yet most emphasize low-resource languages like English. |
| Approach: | They propose a benchmark for massive multitask language understanding in Bengali . they use a dataset that preserves mathematical content via MathML and a subset of questions most frequently missed by top systems to stress difficult cases. |
| Outcome: | The proposed benchmark covers 24 model variants across 11 LLM families. |
Do Multi-Agents Solve Better Than Single? Evaluating Agentic Frameworks for Diagram-Grounded Geometry Problem Solving and Reasoning (2026.eacl-srw)
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Mahbub E Sobhani, Md. Faiyaz Abdullah Sayeedi, Mohammad Nehad Alam, Proma Hossain Progga, Swakkhar Shatabda
| Challenge: | Diagram-grounded geometry problem solving is critical for multimodal large language models, but the benefits of multi-agent design over single-aggent remain unclear. |
| Approach: | They compare diagram-grounded geometry problem solving to four visual math benchmarks . they found that multi-agent pipelines provide clear benefits for open-source models . |
| Outcome: | Theorem-based solvers and architectural refinements improve performance on four visual math benchmarks. |