LLM Tokenization

Tokenization is a fundamental process in preparing text for Large Language Models (LLMs). It converts input text into smaller units called tokens, which are the basic elements LLMs use for analysis and generation.

The Tokenization Process

1. Preprocessing: Cleaning up the text
2. Remove special characters
3. Eliminate common words (stop words) like "the" or "and"

4. Trim extra whitespace

5. Initial Segmentation:

6. Split text into sentences

7. Break sentences into words

8. Main Tokenization: Two primary approaches

9. Word-level: Divide text into individual words

10. Subword-level: Break words into smaller, meaningful parts

11. Token Representation:

12. Each token gets a unique ID
13. IDs correspond to entries in the model's vocabulary

Example: Input: "The quick brown fox jumps over the lazy dog" Tokens: ["The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"] Token IDs: [1, 2, 3, 4, 5, 6, 7, 8, 9]

1. Vocabulary: The model's token dictionary
2. Contains all unique tokens the model knows
3. Created during model training
4. Maps tokens to their IDs

Tokenization Strategies and Techniques

• Word Tokenization: Splits text into whole words
• Subword Tokenization: Breaks words into smaller units
• Byte Pair Encoding (BPE): Popular subword algorithm
• WordPiece: Alternative subword method
• Unigram: Approach based on single units of meaning

By converting text into tokens, LLMs can effectively process and understand language input, leading to more accurate analysis and generation of human-like text.