What is the primary purpose of using tokens in language models?

The primary purpose of using tokens in language models is to enable those models to understand and generate text. Tokens are essentially the smallest units of meaningful text, which can be words, parts of words, or even characters, depending on the tokenization approach used by the language model. By breaking down the text into these units, the model can effectively process and analyze language data. This facilitates tasks such as language understanding, translation, and text generation by allowing the model to recognize patterns, context, and relationships within the text.

Tokens allow the language model to navigate the complexities of human language, capturing nuances such as grammar, semantics, and syntax. This is essential for generating coherent and contextually appropriate responses or outputs. Therefore, tokens are fundamental for the functioning of language models and their application in various natural language processing tasks.