Have you ever wondered how large language models like GPT are capable of generating text seamlessly? It all boils down to tokens – the fundamental units that these models operate on. Now, I'm no expert on this stuff, but I've recently learned a thing or two about how tokens work, and let me tell you, I'm absolutely fascinated!

What are tokens?

In the context of natural language processing and GPT, tokens are like the building blocks of text. They take a piece of text and divide it into smaller chunks. These chunks can be words, letters, or even parts of words. Each token is assigned a unique numerical representation and by doing this, GPT can study the patterns and structures in the language.

Why does GPT do this tokenization thing?

Good question! Here's why:

It makes things consistent: Tokens help GPT process text in a consistent way. It's like putting everything in the same format so that GPT can understand and work with it.

It saves memory: Tokens are memory-friendly. They don't take up as much space as storing entire words or characters. This is important because GPT deals with a ton of text, and it needs to be efficient.

It handles tricky language stuff: Language can be complex, right? Tokenization breaks down sentences into smaller pieces, making it easier for GPT to understand what's going on and how different parts relate to each other.

It manages the word collection: GPT has a set vocabulary of tokens it knows. During training, it figures out which tokens to use. If it comes across a word it doesn't know, it has special tokens to handle that situation.

If you're struggling to grasp how tokenization works, don't worry! There's an awesome visual tokenizer created by Simon Willison that can help you visualize it.

The art of tokenization

Breaking down text

Here we have a sentence "I like my tokens like my snacks, bite-sized." This sentence is our playground to understand how tokenization works. When we pass this sentence through the token encoder, it breaks down into smaller tokens, such as "I," "like," "my," "tokens," "snacks," ",", "bite," "-", and "sized." Each token receives its own unique representation, like a secret code that captures its essence.

In this particular example, all the words are small and commonly used in the English language. As a result, each word corresponds to a single token. However, it's important to note that this isn't always the case.

Nuances and quirks

As we explore the above example, we encounter some fancy and less common words. The token encoder has a clever trick up its sleeve to handle them. It breaks down these big words into smaller tokens to capture their true meaning. Check this out: the word "Token" becomes token 22906, but remember in the previous example we had " token" with a space and a lowercase "t"? It turned into token 16326!

This smart move shows how cool tokenization can be. It assigns different tokens to the same word based on fancy stuff like capital letters and leading spaces. This way, the tokenizer can encode each variation accurately and make our sentences look tidy. Plus, it even saves tokens by not giving one to every space in our text. How clever is that?

Conclusion

Tokenization is a powerful technique that breaks down text into smaller units, enabling language models like GPT to understand and generate human-like text. By assigning unique tokens to words, considering factors like capitalization and spacing, tokenization captures the intricacies of language with remarkable accuracy. It not only enhances the efficiency of encoding full sentences but also handles complex word variations intelligently.

If you're craving more tokenizer secrets and curious about the quirks and anomalies, I highly recommend diving into Simon Willison's in depth blog post.