MC

How do language models "see" text?

Understand how text becomes tokens in language models and how this affects your AI applications.

Click on an example to see how different types of text are tokenized differently:

Select Tokenization Method

Different tokenization methods have varying trade-offs in efficiency and accuracy.

How do language models "see" text?

See how your text is broken into tokens in real-time. Select different tokenization methods to understand their differences.

Type something to see tokenization in action...

Tiktoken (OpenAI)

OpenAI's optimized tokenizer used for their models like GPT-3.5 and GPT-4. It's a BPE variant that's been trained on a large corpus and optimized for efficiency.

Pros

  • Fast and efficient
  • Used by OpenAI models
  • Well-optimized for English and code

Cons

  • Closed vocabulary
  • May not handle some languages well
  • Specific to OpenAI models

Test Your Understanding

Apply what you've learned with these interactive challenges.

Tokenization Quiz

1. Which tokenization method processes text one character at a time?

A
Word tokenization
B
Character tokenization
C
BPE
D
Tiktoken

2. What is the main advantage of subword tokenization methods like BPE?

A
They process text faster than other methods
B
They always use fewer tokens than word tokenization
C
They handle rare words by breaking them into subword units
D
They don't require a vocabulary

3. Which tokenizer is used by OpenAI's GPT models?

A
WordPiece
B
SentencePiece
C
Tiktoken
D
Byte-level BPE

4. Why does tokenization matter for developers using LLMs?

A
It affects API costs and context window limitations
B
It determines how fast the model can generate text
C
It controls the temperature parameter
D
It enables model fine-tuning

5. Which of these would typically require the most tokens when processed by a subword tokenizer?

A
Common English phrases
B
Technical terminology and rare words
C
Simple numeric sequences
D
Short code snippets