Efficient Tokenization

Efficient tokenization, the process of breaking down text into meaningful units for language models, is a crucial area of research aiming to improve model performance and reduce computational costs. Current efforts focus on refining existing algorithms like Byte Pair Encoding (BPE), developing novel methods that incorporate linguistic knowledge or learn tokenizations end-to-end, and optimizing tokenizers for specific tasks or languages. These advancements are significant because improved tokenization directly impacts the accuracy, efficiency, and adaptability of language models across various applications, from machine translation to biomedical text analysis.