?? Decoding Tokenization: The Building Block of Large Language Models (LLMs) ??

Today, let’s dive into one of the foundational aspects of LLMs: Tokenization.

Imagine taking a vast, complex puzzle (text) and breaking it into smaller, manageable pieces—these pieces are called tokens. Tokens are the fundamental units that LLMs process, making them the input and output layers' currency. Let's explore the different methods of tokenization and their nuances:

1?? Word Tokens

?? What it does: Splits text into individual words.

?? Example: "Tokenization is fun" → ["Tokenization", "is", "fun"].

?? Drawback: Struggles with out-of-vocabulary (OOV) words. For instance, it wouldn’t know what to do with “AImazing” if it wasn’t in its vocabulary.

?? Fun Fact: Word2Vec used this method back in the day!

2?? Subword Tokens

?? What it does: Breaks text into partial words when needed.

?? Example: "AImazing" → ["AI", "##mazing"].

?? Advantage: Handles OOV words by breaking them into known subword components.

?? Pro tip: A special symbol (e.g., “##”) indicates a token is a continuation of the previous token.

3?? Character Tokens

?? What it does: Breaks text into individual characters.

?? Example: "Token" → ["T", "o", "k", "e", "n"].

?? Advantage: Handles all new words effortlessly.

?? Challenge: Computationally expensive and requires longer training times.

4?? Byte Tokens

Byte Tokens

?? What it is: Splits text into bytes, the smallest data units, using Byte Pair Encoding (BPE).

?? How it works: Frequently occurring byte pairs are merged iteratively until a fixed vocabulary size is reached.

?? Pros:

1. Handles unseen words and rare symbols (e.g., emojis, tabs).
2. Supports multilingual text efficiently.

?? Cons:

1. Less intuitive to interpret individual tokens.
2. Training can be slower for longer tokens.

?? Used in: GPT-4, StarCoder2, and Llama2 for tasks ranging from natural language processing to code generation.

Tokenization in Action: LLM Highlights ??

BERT (2018)

?? Method: WordPiece (a variant of BPE). ?? Features:

• Converts text to lowercase.
• Uses special tokens like [CLS] for classification tasks.
• Captures sentence-level meaning effectively. ?? Example: "Capitalization" → ["capital", "##ization"].

GPT-4

?? Method: BPE. ?? Features:

• Preserves capitalization and special symbols.
• Introduces newline tokens (useful for structured text).
• Excels in coding tasks, handling tabs and special characters. ?? Notable Special Token: "Fill-in-the-middle" enables it to generate code by understanding the context from both preceding and succeeding text.

Example

def fibonacci(n: int):  
    """Return the Fibonacci series till n."""  
    # Before code is prefix...  
    # <LLM generates the logic for Fibonacci series here>  
    # After code is suffix  
n = int(input("Enter a number: "))  
print(fibonacci(n))          

GPT-4 fills in the missing logic, like:

def fibonacci(n: int):  
    """Return the Fibonacci series up to n."""  
    sequence = [0, 1]  
    while sequence[-1] + sequence[-2] < n:  
        sequence.append(sequence[-1] + sequence[-2])  
    return sequence          

StarCoder2

?? Specializes in generating code! ?? Features:

• BPE-based tokenization.
• Manages context across multiple files using special tokens for repository and file names.
• Great for navigating codebases and collaborating in repositories.

LLaMA2

?? Features:

• BPE-based tokenization.
• 32K vocabulary size—big enough to handle complex tasks!
• Special tokens for chat applications:<|user|><|assistant|><|system|>

?? Takeaway: Tokenization is the secret sauce of LLMs, converting text into a format they can understand while retaining meaning. It’s fascinating how each model tailors tokenization to its use case, from conversational AI to complex code generation.

?? What’s your favorite tokenization method? Or, which of these LLMs fascinates you the most? Let’s discuss in the comments!

Stay tuned for more deep dives into the world of LLMs! ??

#Tokenization #AI #LargeLanguageModels #GenerativeAI #NLP

Pictures and contents are taken from Chapter 2 of Jay Alammar's book - Hands On Large Language Model