Context and Tokens

In LLMs, context and tokens are fundamental concepts that dictate how models interpret and generate human-like text. Grasping these concepts is crucial for anyone looking to utilize LLMs effectively.

Think of it this way:

• Tokens as Building Blocks: Imagine tokens as the individual words or pieces of words that make up sentences—like the bricks that build a house. Each token is a unit of meaning the model understands.

• Context as the Surrounding Environment: Context is like the surrounding environment where a conversation takes place. It includes all the prior information that gives meaning to the current interaction.

Key Points about Tokens and Context:

1. Tokens:

   • Definition: Tokens are the smallest units of text that the model processes. They can be whole words or subword units, depending on the tokenization method.
   • Tokenization: The process of converting raw text into tokens. Common methods include Byte Pair Encoding (BPE) and WordPiece, which break down words into subword units for better handling of rare or unknown words.
   • Model Input: LLMs receive input as a sequence of tokens, each mapped to numerical representations (embeddings) that the model can process.

2. Context:

   • Definition: Context refers to the textual information surrounding a particular token or sequence of tokens. It provides background and situational information that influences how the model interprets and generates text.
   • Context Window: LLMs have a fixed context window size (e.g., 2048 tokens in GPT-3), which is the maximum number of tokens the model can consider at once.
   • Role in Predictions: The model uses context to predict the next token in a sequence, ensuring that generated text is coherent and relevant to the preceding content.

3. Importance of Context and Tokens:

   • Understanding Ambiguity: Words can have multiple meanings, and context helps the model determine the intended meaning.
   • Maintaining Coherence: In longer texts or conversations, context ensures that references and pronouns correctly relate to previous information.
   • Sequential Dependency: Language is sequential; each token’s meaning can depend on the tokens that come before (and sometimes after) it.

4. Limitations:

   • Context Window Limit: If the input text exceeds the model’s context window, earlier tokens may be truncated, potentially losing important information.
   • Computational Resources: Larger context windows require more memory and computational power, impacting performance and cost.

Why Are Tokens and Context Important in LLMs?

• Accurate Language Generation: Tokens are the basic units that models manipulate to generate text. Proper tokenization affects the model’s ability to handle complex words and languages.
• Relevance and Coherence: Context ensures that the generated text is not just grammatically correct but also relevant to the preceding conversation or text.
• Enhanced Understanding: For tasks like translation, summarization, or question-answering, context allows the model to capture nuances and provide more accurate outputs.

In Simple Terms:

• Tokens are like the letters and words you use to write sentences—the basic elements of language that the model reads and writes.
• Context is like the storyline in a book that helps you understand why characters act the way they do; it provides the background that gives meaning to the tokens.

A simple tool to visualize tokenization is available at https://tiktokenizer.vercel.app/?model=meta-llama%2FMeta-Llama-3-8B. (Note: This tool is for demonstration purposes and may not have all the models we use.)