AI Term 4 min read

Token

The basic unit of text processing in natural language models, representing words, subwords, or characters that AI systems use to understand and generate language.

Token

A Token is the fundamental unit of text processing in natural language processing and AI systems. Tokens represent discrete pieces of text—such as words, subwords, punctuation marks, or even characters—that models use to understand, process, and generate human language.

What are Tokens?

Basic Definition Tokens are the building blocks that AI models use to process text:

  • Smallest meaningful units for the model
  • Can represent full words, partial words, or characters
  • Converted to numerical representations for processing
  • Essential for all text-based AI operations

Tokenization Process The conversion of raw text into tokens:

  1. Text preprocessing and normalization
  2. Splitting text according to tokenization rules
  3. Mapping tokens to unique numerical identifiers
  4. Creating sequences that models can process

Types of Tokenization

Word-Level Tokenization Each word becomes a separate token:

  • Simple and intuitive approach
  • “Hello world” → [“Hello”, “world”]
  • Challenges with rare words and morphology
  • Large vocabulary size requirements

Subword Tokenization Breaking words into smaller meaningful parts:

  • Handles rare and unseen words better
  • “unhappiness” → [“un”, “happiness”] or [“unhapp”, “iness”]
  • Balances vocabulary size and coverage
  • Most common approach in modern models

Character-Level Tokenization Each character is a token:

  • Very small vocabulary size
  • Can handle any text in the language
  • Longer sequences for the same content
  • Less efficient for capturing meaning

Byte Pair Encoding (BPE)

  • Iteratively merges most frequent character pairs
  • Creates subword units based on frequency
  • Used by GPT models and many others
  • Good balance between efficiency and coverage

WordPiece

  • Similar to BPE but uses likelihood-based merging
  • Developed by Google for BERT
  • Optimizes for language model probability
  • Handles morphologically rich languages well

SentencePiece

  • Language-agnostic tokenization
  • Treats text as raw byte sequences
  • No pre-processing or language-specific rules
  • Used by T5, XLNet, and multilingual models

Token Limits and Context Windows

Context Length The maximum number of tokens a model can process:

  • GPT-3.5: ~4,000 tokens
  • GPT-4: 8,000-32,000 tokens
  • Claude: Up to 200,000 tokens
  • Determines conversation length and document size

Token Counting Approximating token usage:

  • English: ~4 characters per token on average
  • 100 words ≈ 75-100 tokens
  • Punctuation often counts as separate tokens
  • Varies by language and tokenizer

Token Economics

API Pricing Many AI services charge by token usage:

  • Input tokens (prompt) + output tokens (response)
  • Different rates for different models
  • Optimization strategies to reduce costs
  • Batch processing for efficiency

Efficiency Considerations

  • Shorter prompts = lower costs
  • Efficient tokenization reduces overhead
  • Token-aware prompt engineering
  • Caching frequently used content

Technical Implementation

Token IDs Numeric representation of tokens:

  • Each token maps to a unique integer
  • Special tokens for padding, start, end
  • Vocabulary indices for model lookup
  • Consistent mapping across model usage

Vocabulary Management

  • Fixed vocabulary size for each model
  • Unknown token handling for out-of-vocabulary words
  • Special tokens for model operations
  • Regular vocabulary updates and expansions

Challenges and Considerations

Multilingual Support

  • Different languages have varying tokenization needs
  • Script-specific considerations (Arabic, Chinese)
  • Balancing vocabulary across languages
  • Handling code-switching and mixed scripts

Domain Adaptation

  • Technical terminology and jargon
  • Specialized vocabularies for specific fields
  • Handling abbreviations and acronyms
  • Custom tokenization for specific use cases

Bias and Fairness

  • Tokenization can introduce bias
  • Unequal treatment of different languages
  • Representation quality across demographics
  • Impact on model performance fairness

Best Practices

Prompt Design

  • Be aware of token consumption
  • Use concise, clear language
  • Avoid unnecessary repetition
  • Consider token-efficient alternatives

Model Selection

  • Choose models with appropriate context lengths
  • Balance performance and token limits
  • Consider specialized models for long documents
  • Evaluate tokenization quality for your use case

Optimization Strategies

  • Cache common prompts and responses
  • Use summarization for long documents
  • Implement efficient batching strategies
  • Monitor token usage patterns

Understanding tokens is crucial for effective AI system design, cost optimization, and ensuring that applications work efficiently within model constraints and capabilities.

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