TigerLLM是首个超越主流大模型的孟加拉语大模型家族。
TigerLLM - A Family of Bangla Large Language Models
- 基于本土化数据构建孟加拉语大模型，提升语言适配性
- 在标准评测中表现超越所有开源模型及GPT3.5
- 为低资源语言建模提供可复现的新基准
大型语言模型的发展严重偏向英语及其他少数高资源语言。这一语言不平等在孟加拉语——全球第五大使用语言——中尤为突出。尽管已有少数尝试构建开源孟加拉语大模型，但其性能仍落后于高资源语言，且可复现性有限。为此，我们推出TigerLLM——一组孟加拉语大模型。实验表明，这些模型在标准基准测试中超越所有现有开源替代方案，并优于更大规模的专有模型如GPT3.5，确立了TigerLLM作为未来孟加拉语建模的新基准。
原文摘要 · Abstract (English)
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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