Evaluating Tokenizer Performance of Large Language Models Across Official Indian Languages

• URL: http://arxiv.org/abs/2411.12240v2
• Date: Tue, 26 Nov 2024 18:14:50 GMT
• Title: Evaluating Tokenizer Performance of Large Language Models Across Official Indian Languages
• Authors: S. Tamang, D. J. Bora,
• Abstract summary: This paper presents a comprehensive evaluation of tokenizers used by 12 Large Language Models (LLMs) across all 22 official languages of India. The SUTRA tokenizer outperforms all other models, including several Indic-specific models, excelling in 14 languages. This study underscores the critical importance of developing targeted tokenization strategies for multilingual and Indic-centric models.
• Score: 0.0
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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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