Year: 2025
Type: conference-paper
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 5 th most spoken language.A few initiatives attempted to create opensource 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.
Authors: Nishat Raihan, Marcos Zampieri
Institution: George Mason University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412889533