TigerLLM - A Family of Bangla Large Language Models

Nishat Raihan, Marcos Zampieri


Abstract
The development of Large Language Models (LLMs) remains heavily skewed towards English and a few other high-resource languages. This linguistic disparity is particularly evident for Bangla - the 5th most spoken language. A few initiatives attempted to create open-source Bangla LLMs with performance still behind high-resource languages and limited reproducibility. To address this gap, we introduce TigerLLM - a family of Bangla LLMs. Our results demonstrate that these models surpass all open-source alternatives and also outperform larger proprietary models like GPT3.5 across standard benchmarks, establishing TigerLLM as the new baseline for future Bangla language modeling.
Anthology ID:
2025.acl-short.69
Volume:
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2025
Address:
Vienna, Austria
Editors:
Wanxiang Che, Joyce Nabende, Ekaterina Shutova, Mohammad Taher Pilehvar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
887–896
Language:
URL:
https://preview.aclanthology.org/landing_page/2025.acl-short.69/
DOI:
Bibkey:
Cite (ACL):
Nishat Raihan and Marcos Zampieri. 2025. TigerLLM - A Family of Bangla Large Language Models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 887–896, Vienna, Austria. Association for Computational Linguistics.
Cite (Informal):
TigerLLM - A Family of Bangla Large Language Models (Raihan & Zampieri, ACL 2025)
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PDF:
https://preview.aclanthology.org/landing_page/2025.acl-short.69.pdf