AI Term:Token, Token Cost

In the context of language models like GPT-3 or ChatGPT, a “token” usually refers to a chunk of text that the model reads or writes at a time. The specific definition of a token depends on the language and the tokenization process, but in English, a token is often a word or part of a word.

Here’s a more detailed look at tokens and token costs:

1. Tokenization: This is the process of breaking down text into tokens. Different models use different methods of tokenization. For instance, some models break text down into individual words, while others break it down into subwords or characters. GPT-3 and ChatGPT use a byte pair encoding (BPE) tokenization, which can break words into smaller pieces. For example, the word “unhappiness” might be broken down into three tokens: “un”, “happiness”, and “s”.
2. Token Cost: The token cost refers to the number of tokens that are used in an operation. For instance, if you’re using an API to interact with a language model, you might be billed based on the number of tokens in your input and output. Both the prompt (input) and the model’s response (output) contribute to the total token count.
3. Limitations: Language models like GPT-3 have a maximum limit on the number of tokens they can handle in a single operation (for GPT-3, this is 4096 tokens as of my knowledge cutoff in September 2021). If a conversation has too many tokens to fit within this limit, some of the text will have to be trimmed or omitted.
4. Influencing Model Behavior: The number of tokens in a model’s output can often be controlled by setting a parameter when making a request to the model. For example, you can limit the length of the model’s responses by setting a maximum token limit.

In essence, tokens are a fundamental concept in natural language processing, influencing everything from how a model reads and writes text, to how users are billed for using an API, to how the model’s behavior can be controlled.