LLM Module 0 - Introduction | 0.5 Tokenization

Introduction

Tokenization is a fundamental concept in Natural Language Processing (NLP) that involves breaking down text into smaller, manageable units called tokens. These tokens can be words, characters, or subwords, depending on the design choices made for a given NLP model. The purpose of tokenization is to convert the textual data into a digital format that can be easily processed by computation.

The Process of Tokenization

The tokenization process can be broken down into two main steps:

1. Creating a Vocabulary: The first step involves building a vocabulary from the training dataset. This can include every word in a language's dictionary. For example, we could convert the English dictionary into an indexed format, assigning each word a unique number, starting from zero. This creates a list of indices that can then be used whenever new sequences of tokens are encountered.

2. Encoding Tokens as Indices: When we have a phrase or sentence, we can convert each token (word) into its corresponding index. This allows us to transform textual input into a sequence of numerical values that our models can process.

Limitations of Word Tokenization

While word tokenization is straightforward, it comes with some challenges. If the vocabulary built from the training set misses certain words (common or uncommon), encountering these out-of-vocabulary (OOV) tokens during model usage will lead to errors. Additionally, this method becomes inflexible in the face of misspellings or newly created words. As a result, word tokenization can lead to very large vocabularies because each form of a word (e.g., "fast," "faster," "fastest") requires separate tokens.

Character Tokenization: Pros and Cons

One alternative to word tokenization is character tokenization. This method uses individual characters as tokens, simplifying the vocabulary size significantly (for instance, about 100 characters in English, including both upper and lower case, punctuation, and digits). The advantages of character tokenization include:

• Flexibility to create new words and account for misspellings.

However, this approach has significant downsides. By breaking words down into characters, we lose the inherent meaning that words convey. Additionally, sequences become considerably longer (for example, "The moon" would transform into many more tokens), which is inefficient for processing.

The Middle Ground: Subword Tokenization

Subword tokenization represents a compromise between word and character tokenization. In this method, words are broken down into meaningful subunits or parts. For instance, the word "subject" could be split into "sub" and "ject," allowing the model to handle variations and related words like "subjective," "subordinate," and "submarine" effectively.

Popular techniques for subword tokenization include:

• Byte Pair Encoding: A popular method for constructing vocabularies of subwords.
• SentencePiece: Another approach designed for subword tokenization.
• WordPiece: Often used in state-of-the-art language models.

These strategies strike a balance between vocabulary size and flexibility while preserving sufficient meaning from the words.

Subword tokenization has become the predominant approach in modern large language models due to its effective balance of token count and vocabulary size.

Conclusion

Tokenization is a crucial step in NLP that lays the groundwork for further language processing efforts. In our next discussion, we will delve into the concept of word embeddings, focusing on how we can incorporate meaning and context into our tokenized data.

Keywords

• Tokenization
• Natural Language Processing (NLP)
• Vocabulary
• Out-of-Vocabulary (OOV)
• Word Tokenization
• Character Tokenization
• Subword Tokenization
• Byte Pair Encoding
• SentencePiece
• WordPiece
• Word Embeddings

FAQ

Q: What is tokenization in NLP?
A: Tokenization is the process of breaking down text into smaller units called tokens, which can be words, characters, or subwords, to prepare the data for computational analysis.

Q: Why is vocabulary important in tokenization?
A: Vocabulary is critical because it maps each token to a unique index, allowing the model to convert textual data into numerical values that can be processed.

Q: What are the limitations of word tokenization?
A: Limitations include the possibility of out-of-vocabulary errors, inflexibility regarding misspellings or new words, and very large vocabularies due to needing separate tokens for different forms of a word.

Q: How does character tokenization differ from word tokenization?
A: Character tokenization uses individual characters as tokens, leading to a smaller vocabulary but longer sequence lengths and a loss of meaning related to words.

Q: What is subword tokenization?
A: Subword tokenization is a method that breaks down words into smaller meaningful units, allowing for flexible handling of variations and related words without creating an excessively large vocabulary.