Evaluating Tokenizer Performance of Large Language Models Across Official Indian Languages

S. Tamang; D. J. Bora

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arXiv · 2411.12240

Evaluating Tokenizer Performance of Large Language Models Across Official Indian Languages

S. Tamang·D. J. Bora

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Abstract

Large Language Models (LLMs) based on transformer architectures have revolutionized a variety of domains, with tokenization playing a pivotal role in their pre-processing and fine-tuning stages. In multilingual models, particularly those tailored for Indic languages, effective tokenization is crucial for optimizing performance. This paper presents a comprehensive evaluation of tokenizers used by 12 LLMs across all 22 official languages of India, with a focus on comparing the efficiency of their tokenization processes. We employed the Normalized Sequence Length (NSL) as a key metric in our analysis. Our findings reveal that the SUTRA tokenizer outperforms all other models, including several Indic-specific models, excelling in 14 languages. Notable insights include the SUTRA tokenizer's superior handling of Indic languages, GPT-4o's advancement over its predecessor GPT-4 in processing Indian languages, and the limited performance of Project Indus in certain languages. This study underscores the critical importance of developing targeted tokenization strategies for multilingual and Indic-centric models, laying the groundwork for future improvements in tokenizer design to enhance linguistic coverage and model efficiency.

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S. Tamang, D. J. Bora. 2024-11-19. Evaluating Tokenizer Performance of Large Language Models Across Official Indian Languages. https://arxiv.org/abs/2411.12240

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