Originally written in 2023. Republished here.

Tokenizers are essential components of generative AI models, such as GPT-4, that can create various types of content, such as text, code, music, and images. Tokenizers are responsible for converting the input and output data into a format that the model can understand and manipulate. In this article, we will explore what tokenizers are, how they work, and why they are important for generative AI.

What are tokenizers?

Tokenizers are algorithms that split a given input into smaller units, called tokens, that can be processed by a generative AI model. Tokens can be words, characters, subwords, or even pixels, depending on the type and granularity of the input data. For example, a text tokenizer can split a sentence into words or subwords, while an image tokenizer can split an image into patches or pixels.

The output of a tokenizer is a sequence of tokens, each represented by a unique numerical identifier, called a token ID. The token IDs are then fed into the generative AI model as input or used to decode the output of the model. For example, a text tokenizer can map the word "hello" to the token ID 1234, and the word "world" to the token ID 5678. The input sequence [1234, 5678] can then be used to generate a new text output, such as [7890, 4321], which can be decoded back to words using the same tokenizer.

How do tokenizers work?

Tokenizers can be implemented in different ways, depending on the type and complexity of the input data. Some common types of tokenizers are:

• Character-level tokenizers: These tokenizers split the input into individual characters, such as letters, digits, punctuation, and symbols. Character-level tokenizers are simple and flexible, but they can result in long sequences of tokens and limited vocabulary size.
• Word-level tokenizers: These tokenizers split the input into words, based on whitespace and punctuation. Word-level tokenizers are intuitive and easy to understand, but they can result in out-of-vocabulary (OOV) tokens and misspellings.
• Subword-level tokenizers: These tokenizers split the input into subwords, which are smaller units of words that capture common prefixes, suffixes, and stems. Subword-level tokenizers are more efficient and robust, as they can handle OOV tokens and rare words, but they can also result in unnatural splits and ambiguity.
• Pixel-level tokenizers: These tokenizers split the input into pixels, which are the smallest units of an image. Pixel-level tokenizers are straightforward and universal, but they can result in high-dimensional and noisy input data.

Why are tokenizers important for generative AI?

Tokenizers are important for generative AI because they enable the model to learn from and generate diverse and complex types of data. Tokenizers can affect the performance and quality of the generative AI model in several ways, such as:

• Data representation: Tokenizers determine how the input and output data are represented and encoded, which can influence the information and structure that the model can capture and manipulate.
• Data processing: Tokenizers determine how the input and output data are processed and decoded, which can influence the speed and efficiency of the model’s computation and generation.
• Data quality: Tokenizers determine how the input and output data are split and mapped, which can influence the accuracy and diversity of the model’s output.

Thank you for reading this article, have fun with generative AI! 🤖