Evaluating Tokenizer Performance of Large Language Models Across
  Official Indian Languages
Authors: name: S. Tamang, name: D. J. Bora

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.