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 nat.io // BLOG POST
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TITLE:    Understanding Tokens in Large Language Models
DATE:     January 18, 2025
AUTHOR:   Nat Currier
TAGS:     AI, Large Language Models, Machine Learning
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Large language models (LLMs) like ChatGPT, GPT-4, and others have transformed how we interact with artificial intelligence (AI). They power chatbots, summarize text, generate creative writing, and more. But behind the scenes of these impressive capabilities lies a fundamental building block: the **token**.

This post will dive deep into what tokens are, how they evolved, why they matter, and the technical reasons behind their existence. We'll also address common misconceptions and explain why understanding tokens is crucial for anyone using LLMs. By the end, you'll have a comprehensive grasp of this key concept—no technical background required.

[ What is a Token? ]
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At its core, a **token** is a unit of text that an LLM uses to understand and generate language. You can think of it as a piece of a sentence. But what exactly counts as a "piece"? It depends on the model. Here's how tokens work:

- **Words or Parts of Words**: Tokens can be entire words (like "apple") or parts of words (like "app" and "le").
- **Punctuation**: Symbols such as "." or "," are also tokens.
- **Whitespace**: Even spaces between words can count as tokens in some systems.

For example, the sentence:

```
I love apples.
```

might be broken into these tokens:

1. "I"
2. "love"
3. "apples"
4. "."

That's four tokens in total.

[ Why Do Tokens Exist? A Brief History ]
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To understand why tokens are necessary, let's step back and look at how language models have evolved.

1. **Early NLP Systems**:
    
    - Older natural language processing (NLP) systems relied on simple rule-based methods or word-level representations.
    - These approaches struggled with complexity. For instance, they couldn't handle rare words or morphologically rich languages efficiently.
2. **Introduction of Embeddings**:
    
    - With the advent of word embeddings like Word2Vec, language models began representing words as continuous vectors. Each word was a point in a high-dimensional space.
    - However, this approach had limitations:
        - It treated each word as an atomic unit, making it hard to generalize across related words (e.g., "running" and "runner").
        - It couldn't handle unseen words, such as new slang or technical jargon.
3. **Subword and Tokenization Revolution**:
    
    - To address these challenges, researchers introduced tokenization methods like Byte Pair Encoding (BPE) and SentencePiece.
    - These techniques split words into smaller units (tokens), allowing models to:
        - Efficiently handle rare or unseen words.
        - Capture relationships between parts of words (e.g., "run" in "running" and "runner").

[ Technical Reasons Behind Tokens ]
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Tokens aren't just arbitrary chunks of text—they serve specific technical purposes:

1. **Efficiency in Training**:
    
    - Breaking text into tokens reduces the vocabulary size. Instead of memorizing millions of whole words, the model learns a smaller set of tokens and combines them to form words or sentences.
    - This makes training computationally feasible and allows models to generalize better.
2. **Handling Rare Words**:
    
    - Consider a rare word like "antidisestablishmentarianism." Without tokenization, the model might struggle because it hasn't seen this exact word during training. Tokenization splits it into smaller, more familiar parts (e.g., "anti", "dis", "establish", "ment").
3. **Language Agnosticism**:
    
    - Token-based systems work well across diverse languages, including those with complex scripts like Chinese or Arabic.
4. **Contextual Understanding**:
    
    - Tokens allow models to focus on context at the level of subwords or characters, improving their ability to disambiguate meanings.

[ Why Tokens Matter for LLM Users ]
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For anyone interacting with LLMs, understanding tokens can:

1. **Help You Optimize Prompts**:
    
    - The cost of using an LLM (in time or money) is directly tied to the number of tokens. Writing concise, clear prompts reduces the token count and ensures you stay within model limits.
2. **Clarify Token Limits**:
    
    - Every LLM has a maximum token capacity (e.g., GPT-4 might handle 8,000 tokens per interaction). This limit includes both your input and the model's output. Understanding this prevents frustration when outputs are cut off.
3. **Avoid Misunderstandings**:
    
    - Some users think LLMs "understand" sentences like humans do. In reality, they process tokens in patterns. Knowing this can help you debug confusing responses.

[ Common Misconceptions About Tokens ]
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Let's debunk a few myths:

1. **"Tokens are just words."**
    
    - Not true. Tokens can be parts of words, punctuation, or even spaces.
2. **"Longer inputs are always better."**
    
    - While context is important, overly long inputs can dilute relevance or exceed token limits.
3. **"Tokenization is the same across models."**
    
    - Different models use different tokenization methods. For example, OpenAI's models use BPE, while Google's use SentencePiece.

[ Variations of the Token Theme ]
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Not all tokens are created equal. Different LLMs handle tokens in various ways:

1. **Character-Based Tokens**:
    
    - Models that break text into individual characters. Example: "chat" becomes "c", "h", "a", "t".
    - Pros: Flexible for any language.
    - Cons: Inefficient (many tokens for a single word).
2. **Word-Based Tokens**:
    
    - Early systems treated entire words as tokens. Example: "chatbot" is one token.
    - Pros: Simple and intuitive.
    - Cons: Struggles with rare or unseen words.
3. **Subword Tokens**:
    
    - Splits words into parts. Example: "chatbot" becomes "chat" and "bot".
    - Pros: Balances efficiency and generalization.
4. **Sentencepiece Tokens**:
    
    - A variation that optimizes tokenization for specific languages or use cases.
    - Pros: Better support for non-Latin scripts.

[ Practical Examples ]
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Let's make this tangible with examples:

- Sentence: "Artificial intelligence is amazing!"
    
    - Tokens: "Artificial", "intelligence", "is", "amazing", "!"
    - Number of tokens: 5
- Sentence: "Don't worry, be happy."
    
    - Tokens: "Don", "'t", "worry", ",", "be", "happy", "."
    - Number of tokens: 7

Notice how contractions and punctuation are tokenized separately.

[ Wrapping Up ]
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Tokens are the unsung heroes of LLMs. They enable efficient processing, allow models to generalize across languages and contexts, and make advanced AI applications possible. By understanding tokens and their role, you'll not only gain insight into how these systems work but also become a more effective user of LLMs.

Remember: Every token counts, so choose your words (and pieces of words) wisely!