Multilingual Tokenizer

Multilingual tokenizers are crucial components of large language models (LLMs) designed to process multiple languages, aiming to improve performance and efficiency across diverse linguistic contexts. Current research focuses on optimizing tokenizer training strategies, including novel algorithms and data preprocessing techniques, to address issues like language imbalance and inefficient tokenization in low-resource languages. This work is significant because improved multilingual tokenizers are essential for building truly multilingual LLMs capable of handling the world's linguistic diversity, impacting applications ranging from machine translation to cross-lingual information retrieval. The effectiveness of different tokenizer architectures and the impact of vocabulary size on downstream performance are also active areas of investigation.