The authors release eight Bengali benchmarks translated from English and report that models with more fragmented Bengali tokenization tend to score lower.
TigerLLM - A Family of Bangla Large Language Models
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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.
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Evaluating LLMs' Multilingual Capabilities for Bengali: Benchmark Creation and Performance Analysis
The authors release eight Bengali benchmarks translated from English and report that models with more fragmented Bengali tokenization tend to score lower.