Tokenization

Tokenization is the process of breaking text into smaller pieces called tokens—such as words or subwords—that a language model can understand. For example, “ChatGPT” might become “Chat” and “GPT.” These tokens are then converted into numbers the model uses to process language. Tokenization affects how much text a model can handle at once, how fast it runs, and how accurate its output is. In short, it’s the first step in helping AI read and work with language.

Frequently Asked Questions about Tokenization

1. What is tokenization in natural language processing?‍

Tokenization is the process of breaking text into smaller pieces called tokens like words or subwords so a language model can understand it. It’s the first step that helps AI read and work with language.

2. Can you give a simple example of tokenization?‍

Yes. A term like “ChatGPT” might be split into two subwords: “Chat” and “GPT.” These tokens are then turned into numbers the model can process.

3. Why does tokenization matter for AI model performance?‍

Tokenization affects how much text a model can handle at once, how fast it runs, and how accurate its output is. Better tokenization choices can improve efficiency and results.

4. What kinds of pieces become tokens words or smaller parts?‍

Tokens can be full words or subwords. The goal is to represent text in pieces the model can reliably understand and process.

5. How do tokens help the model actually process language?‍

After text is split into tokens, those tokens are converted into numbers. The model works with these numeric representations to understand and generate language.

6. Where does tokenization fit in the AI pipeline?‍

It comes first. Tokenization is the initial step before any further processing, helping prepare the text so the model can handle it efficiently and accurately.