The discrete units of text that language models process
Core Idea: Tokens are the fundamental units that language models use to process text, representing words, parts of words, or individual characters that are converted into numerical values for model processing.
Key Elements
Tokenization Process
- Text is broken down into tokens according to a defined vocabulary
- A vocabulary typically contains 50,000-200,000 possible tokens
- Common words may be single tokens, while rare words are split into multiple tokens
- Tokenization is language-dependent and optimized for specific language patterns
- Special tokens mark the beginning/end of messages or indicate specific functions
Token Characteristics
- Each token is assigned a unique ID number for model processing
- English words average roughly 4 characters per token (varies by language)
- Spaces, punctuation, and formatting characters also consume tokens
- The same word may be tokenized differently depending on capitalization, spacing, or surrounding context
- Special characters, emojis, and non-Latin scripts often require more tokens per character
Technical Implementation
- Tokenization happens before the text reaches the neural network
- Tokens are converted to vector embeddings for model processing
- Model size and capabilities are often described by the number of tokens they were trained on
- Token limits are a key constraint in model design and usage
Practical Considerations
- Token usage directly impacts computational cost and processing time
- More tokens = more expensive computation
- Context windows have fixed token limits (e.g., 8K, 32K, 128K tokens)
- Effective prompt engineering involves efficient token usage
- Both input and output consume tokens from the available context window
Connections
- Related Concepts: LLM Context Window (the space where tokens exist and are processed), Embeddings (vector representations of tokens)
- Broader Context: NLP Preprocessing (tokenization as part of the text processing pipeline)
- Applications: Token-Efficient Prompting (techniques to minimize token usage), Prompt Engineering (crafting inputs with token constraints in mind)
- Components: BPE Tokenization (a common tokenization algorithm), Vocabulary Size (impact on model capabilities)
References
- Tiktoken documentation (OpenAI's tokenizer)
- "Attention Is All You Need" paper (introducing transformer models that process tokens)
- OpenAI API documentation on token counting and limits
#LLM #tokens #tokenization #NLP #vocabulary
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