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Decoding the Secret to LLM Success: Mastering Tokenization Techniques
LLMs are changing the game, but to harness their full power, you need to understand tokenization. In this article, we’ll explain BytePair, WordPiece, and SentencePiece encoding, the secret sauce behind LLM success. Let’s learn how these techniques work and how to use them to take your LLM projects to the next level.
What is Tokenization??
Tokenization is the process of breaking up a piece of text into smaller units called tokens, which are usually words or subwords. This is an essential step for understanding and analyzing text data. Think of tokenization as cutting a long piece of cloth into smaller pieces (tokens) for easier handling and sewing.
In the context of text, tokenization is used to preprocess and organize text data by breaking it into standard units, such as words or subphrases. This enables computers and algorithms to work with the text more effectively. For example, consider the following sentence: “I love to play games on my laptop.”
- Word-based tokenization: In this case, the sentence is divided into individual words: “I”, “love”, “to”, “play”, “games”, “on”, “my”, “laptop”.
2. Subword-based tokenization (2-gram): For this example, we’ll use a 2-gram (a subword consisting of 2 characters) tokenization:
“I lo”, “ove to”, “to pl”, “pla y”, “lay g”, “ame s”, “s on”, “on m”, “my l”, “lap top”
Types of Tokenization in LLM
LLM mainly uses following types of tokenization techniques.
Byte Pair Encoding (BPE): Byte Pair Encoding (BPE) is a text compression technique used to build a vocabulary for natural language processing (NLP) tasks. It works by merging the most…
