What Are Tokens in Large Language Models?

Large language models (LLMs) are powerful tools that can generate text, translate languages, and answer questions. But how do these models work with words? The secret lies in something called "tokens". This article will explain what tokens are and how they are used in the world of AI.

What are Tokens?

Tokens are the basic building blocks that LLMs use to process and create text. Instead of working directly with whole words or sentences, LLMs break down text into smaller pieces. These pieces are the tokens. A token can be a single character, a part of a word, a whole word, or even a piece of punctuation. Think of it like LEGO bricks. You can combine individual bricks to create bigger and more complex structures. Similarly, LLMs use tokens to build and process text.

The way text is broken into tokens varies between different LLMs. There isn't one single rule that all models follow. This process of breaking text into tokens is called tokenization. Tokenization depends on how the AI was trained and on the language it is working with. For example, a common method is to split words, and then frequently used words or parts of words become separate tokens. Words that appear less often might get further divided into smaller units.

For instance, the word "unbelievable" might be tokenized as "un", "believe", and "able". The word "cats" could be a single token. Punctuation, like commas or periods, often become individual tokens. Some AI models might even tokenize "don't" into "do" and "n't", while other models could treat "don't" as a single token. This flexibility is why different models treat text slightly differently, even if they are processing the same information.

Why are Tokens Important?

Tokens are vital because they provide a standard way for LLMs to work with text. Computers can't understand human language the way we do. They need text to be turned into numbers. Each token is assigned a unique number, and that numerical code allows the model to process the text. This makes it easy for the model to perform mathematical operations which helps in text analysis and generation.

Also, tokens allow the model to handle various lengths of text efficiently. Imagine if the model had to process whole sentences at a time. It would require more processing power and more time. Instead, by working with smaller units, the models can work with text more quickly and also with smaller processing capacity. This approach allows models to handle very large amounts of text, which is very useful.

The process of tokenization also helps handle different languages. Many languages have complex grammar and word structures. Because tokens can be flexible, models can adapt to these variations. This flexibility is useful for tasks such as language translation.

How Tokens Affect Text Generation

The use of tokens greatly affects how LLMs create text. When a model generates text, it is actually creating a sequence of tokens. These tokens are then combined back together to form words, sentences, and paragraphs. The model predicts which token should come next, based on the previous tokens in the sequence. So if the prompt was "The cat sat on", the model will generate the next most likely word, like "mat" which itself will be a single or multiple tokens, depending on the tokenization process.

The tokenization process used by an LLM directly impacts the cost of using that model. Most providers charge based on the number of tokens used when processing a request or generating a response. Therefore, shorter prompts and responses will usually cost less. Some models have a limit to the number of tokens they can process in one go. If the input text is very long, it will need to be broken into smaller segments.

Tokens and the Future of AI

Tokens are a basic, yet important, piece of how LLMs work. They allow these models to handle the complexities of human language efficiently. As AI models grow more powerful, the token system will continue to be crucial for processing and creating text. The way tokens are defined and used can have a large impact on how models perform in the future. It is one area in which there may be more changes as the field progresses. Understanding tokens is a good first step for those wanting to learn more about how these models function.