TigerLLM - A Family of Bangla Large Language Models

Fuente: arXiv
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Autori principali: Raihan, Nishat, Zampieri, Marcos
Natura: Preprint
Pubblicazione: 2025
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author Raihan, Nishat
Zampieri, Marcos
author_facet Raihan, Nishat
Zampieri, Marcos
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2503_10995
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle TigerLLM - A Family of Bangla Large Language Models
Raihan, Nishat
Zampieri, Marcos
Computation and Language
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.
title TigerLLM - A Family of Bangla Large Language Models
topic Computation and Language
url https://arxiv.org/abs/2503.10995