Tokenization

What Is Tokenization?

Tokenization is a fundamental preprocessing step in natural language processing. Before a language model can understand text, it must break it into tokens—manageable pieces that the model can process. Different tokenization strategies exist: word-level tokenization splits on spaces, subword tokenization breaks words into meaningful pieces, and character-level tokenization works with individual characters.

Modern language models use subword tokenization (like Byte-Pair Encoding), which balances vocabulary size with expressiveness. This approach efficiently handles rare words and different languages. Tokenization is important because it directly affects model efficiency: fewer tokens mean faster processing and lower costs, while too-aggressive tokenization loses important information.

Understanding tokenization is crucial when working with language models. Different models use different tokenizers, affecting how text is converted to tokens and thus affecting costs and performance. For example, GPT-4 tokenizes English more efficiently than code, so code-heavy prompts use more tokens and cost more.