Understanding the Importance of Tokenization in Language Models

Ever wondered how language models handle your code? Let’s dive into the world of tokenization, the process of breaking down text into meaningful units, called tokens.

Imagine a Python snippet. In some models like GPT-2, each space is treated as a separate token. So, when the model tries to process this text, it has to handle each space individually. They all feed into the model one by one in the sequence. Sounds efficient? Not really. This method of tokenization can be extremely wasteful.

Here’s the kicker: GPT-2 struggles with Python not because of any inherent issue with the language model itself, but because of the way it tokenizes the code. If you use a lot of indentation with spaces in Python, as is common, you end up bloating the text. It gets spread across too much of the sequence, and we run out of context length in the sequence. Essentially, we’re being too wasteful and taking up too much token space.

But hey, we can change the tokenizer. For instance, the GPT-2 tokenizer creates a token count of 300 for a particular string. If we switch to the GPT-4 tokenizer (CL 100K base), the token count drops to 185. This is because the number of tokens in the GPT-4 tokenizer is roughly double that of the GPT-2 tokenizer. We go from roughly 50k to roughly 100k tokens.

This densification of the input to the model is beneficial because every single token has a finite number of tokens before it that it’s going to pay attention to. So, we’re roughly able to see twice as much text as a context for what token to predict next. However, just increasing the number of tokens isn’t infinitely better. As you increase the number of tokens, your embedding table gets larger, and the softmax at the output, where we try to predict the next token, grows as well. There’s a sweet spot somewhere where you have just the right number of tokens in your vocabulary where everything is appropriately dense and still fairly efficient.

One specific improvement in the GPT-4 tokenizer is the handling of whitespace for Python. It groups more spaces into a single character, making Python representation more efficient. This densifies Python, allowing us to attend to more code before trying to predict the next token in the sequence.

So, the improvement in Python coding ability from GPT-2 to GPT-4 isn’t just a matter of the language model, architecture, and optimization details. A significant part of the improvement also comes from the design of the tokenizer and how it groups characters into tokens. This highlights the importance of not just model parameters, but also the design choices that go into creating these models. It’s a reminder that in the world of AI, every detail matters, and sometimes, it’s the small things that make the biggest difference. So next time you’re coding, remember, it’s not just about the code you write, but also about how your language model reads it.