What is a Token in an LLM?

Tokens are fundamental elements to understand how large language models (LLMs) work. If you have ever used a tool based on an LLM, such as a chatbot or a voice assistant, it is likely that these tools process your requests by breaking them down into "tokens." But what exactly is a token, and why is it essential? Let's explore this concept in a simple and detailed way.

1. Understanding Tokens: A Simple Definition

A token is a unit of text that language models use to understand and generate content. It can be:

• A whole word.
• A part of a word.
• An individual character.

Here is a simple example:

Sentence: "Hello, how are you?"
Possible Tokens: ["Hello", ",", "how", "are", "you", "?"]

The model breaks the sentence into these units to analyze and generate responses.

2. Why Are Tokens Important?

LLMs, like GPT or other models, do not read sentences as we do. They process each sentence in fragments or tokens. These tokens enable the model to:

• Analyze Context: Understand the relationships between words.
• Predict the Next Step: Anticipate which word or fragment should come next.
• Reduce Complexity: Work with uniform units for increased efficiency.

Here is a simple diagram to visualize the process:

3. How Are Tokens Created?

The creation of tokens relies on an algorithm called "tokenization." This process divides text based on specific rules. For example:

• Spaces are often basic separators.
• Punctuation marks, such as "." or ",", can be individual tokens.
• Certain words or parts of words are also isolated.

4. Practical Example:

Let’s see how a more complex sentence is tokenized:

Sentence: "AI models are fascinating!"

Tokens: ["AI", "models", "are", "fascinating", "!"]

Here, some words like "models" remain whole, while punctuation is treated as a separate token. This allows the model to handle even rare or complex words efficiently.

5. Token Limits in LLMs

Each LLM has a maximum capacity for tokens it can process at once. For instance, if a model has a limit of 4,000 tokens, this includes:

• Tokens from the user’s request.
• Tokens in the generated response.

Here is another diagram to explain:

This means that if your request is too long, the response might be truncated.

6. Tokens and Cost: Why It Matters

In a commercial context, the cost of using an LLM is often tied to the number of tokens processed. More tokens mean longer processing times and higher costs. This has direct implications for businesses using AI models:

• Optimization: Write concise requests to reduce costs.
• Efficiency: Prioritize essential information.

7. Simplification: A Simple Metaphor

Imagine a token as a brick. To build a wall (a response or analysis), the model assembles these bricks. The more bricks (tokens) you have, the more complex the wall can be, but it also requires more time and resources.

Conclusion

Tokens are at the heart of how language models operate. By understanding this concept, you can better optimize your interactions with these tools, whether creating efficient chatbots or crafting effective queries. In summary, a token is much more than a simple text fragment: it is the key that enables machines to understand and generate human language.