In the context of Large Language Models (LLMs) like GPT (Generative Pre-trained Transformer), tokens are the basic units of text that the model processes. A token could be a word, part of a word, or even punctuation, depending on how the model is trained and the tokenizer it uses.
What is a Token?
A token is a sequence of characters that represents a basic unit of meaning in a language. In simpler terms, it’s like a “chunk” of text. For example:
· The word "hello" might be a single token.
· The word "unhappiness" might be split into two tokens: "un" and "happiness".
· Even punctuation marks like "." or "," are tokens.
Tokenization Process
Before a language model can understand text, the text is split into tokens in a process called tokenization. For example, the sentence "I love programming." might be tokenized into
["I", "love", "programming", "."]
Some tokenizers break down text even further into subwords or characters depending on the model and its design.
Go to OpenAI tokenizer UI (https://platform.openai.com/tokenizer) and experiment yourself.
How Do Tokens Help Predict the Next Token?
LLMs like GPT are based on the Transformer architecture. These models learn how to predict the next word or token in a sequence by looking at the tokens before it.
When the model is given a sequence of tokens (e.g., "I love"), it predicts the next token in the sequence (e.g., "programming", coding," "learning," or "technology) based on patterns it learned from large amounts of text data.
The model uses these patterns to estimate what token is most likely to follow the given sequence.
Here's how it works:
1. Input Sequence: Let's say we input the sequence "I love" into the model.
o The model sees the tokens "I" and "love".
2. Context Understanding: The model uses attention mechanisms (a key part of the Transformer architecture) to understand the relationship between the tokens in the sequence. It looks at "I" and "love" and understands that "programming" might be a natural next token.
3. Next Token Prediction: Based on the context provided by the previous tokens, the model predicts the next token. The model might predict that "programming" is the most likely token to follow "I love", resulting in the sequence "I love programming".
4. Probability Distribution: For every token the model predicts, it doesn't just choose a single token but calculates a probability distribution over all possible tokens in its vocabulary. So instead of just guessing "programming", it might also consider alternatives like "music", "reading", "coding," "learning," or "technology" with different probabilities.
The predicted token is used as input in the next iteration, creating an expanding window or sliding window pattern. This process is repeated for every token, enabling the model to generate consistent and contextually appropriate text.
This allows the model to generate longer sequences of text where each token prediction is informed by all the tokens that came before it, creating a flow that makes sense within the context.
In Summary,
1. Tokens are pieces of text (like words or parts of words).
2. The model predicts the next token in a sequence based on the tokens that came before it.
3. It uses patterns learned during training to guess what comes next by looking at the context.
4. Each token is part of a larger sequence, and the model uses attention mechanisms to weigh the importance of each token in the sequence to make its predictions.
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