LLM Tokenizers Explained: BPE Encoding, WordPiece and SentencePiece

Summary

This video explains three popular tokenization techniques used in training large language models: Byte Pair Encoding (BPE), WordPiece, and SentencePiece. Tokenization splits text into words or subwords, which are converted to IDs for model processing. BPE merges frequent pairs of characters, WordPiece maximizes likelihood for token pairs, and SentencePiece treats text as a continuous stream of characters. Each method has its unique approach to building token vocabularies, with applications across various languages, including those without explicit spaces like Japanese or Chinese. The video offers a clear breakdown of these tokenization methods and their use cases.

Takeaways

• 😀 Tokenization is the process of splitting text into words or subwords, which are converted into IDs for further processing in language models.
• 😀 Byte Pair Encoding (BPE) merges the most frequent pairs of consecutive characters in a corpus, starting with individual characters and building up the vocabulary iteratively.
• 😀 In BPE, the algorithm continues merging frequent character pairs until the vocabulary reaches a predefined size.
• 😀 WordPiece tokenizer is similar to BPE but merges pairs based on the likelihood of training data, not just frequency.
• 😀 In WordPiece, the merge process aims to maximize the likelihood of token pairs occurring in the training data, enhancing token efficiency.
• 😀 Both BPE and WordPiece create a vocabulary based on subwords and iteratively merge tokens, but they differ in their merging criteria: frequency for BPE and likelihood for WordPiece.
• 😀 The SentencePiece tokenizer addresses language-dependent preprocessing by treating the input text as a continuous stream of characters, including whitespaces.
• 😀 SentencePiece uses the same merging algorithm as BPE but handles languages without explicit spaces (like Japanese or Chinese) more effectively.
• 😀 Unlike BPE and WordPiece, SentencePiece can reconstruct the original sentence by simply concatenating tokens and replacing underscores with spaces.
• 😀 All three tokenizers (BPE, WordPiece, SentencePiece) aim to create a smaller, more efficient vocabulary, but each approach has unique ways of building it based on different criteria.

Q & A

What is tokenization in the context of language models?

Tokenization refers to the process of splitting text into words or subwords, which are then converted into unique IDs through a lookup table. These IDs are used by language models to represent the specific tokens in an embedding matrix.

What is Byte Pair Encoding (BPE) in tokenization?

Byte Pair Encoding (BPE) is a subword tokenization algorithm that recursively merges the most frequent pairs of consecutive characters in a corpus. The process continues until the desired vocabulary size is reached.

How does the BPE tokenizer initialize the vocabulary?

The BPE tokenizer initializes the vocabulary by using all the individual characters found in the sentence, treating them as unique tokens. For example, in the sentence 'deep learning engineer,' the vocabulary would start with the characters 'd,' 'e,' 'e,' 'p,' and so on.

How does BPE determine which pairs of characters to merge?

BPE merges the most frequent consecutive pairs of characters in the text. For instance, if 'in' or 'e' appear most frequently, those pairs are merged and added to the vocabulary.

How does the WordPiece tokenizer differ from BPE?

The WordPiece tokenizer also initializes the vocabulary with individual characters, but instead of merging the most frequent pairs, it merges pairs based on maximizing the likelihood of the training data. This approach aims to find token pairs that are most probable, rather than simply the most frequent.

What does 'maximizing the likelihood of the training data' mean in the context of WordPiece?

Maximizing the likelihood means selecting the token pairs that, when added to the vocabulary, yield the highest probability according to the training data. This method tries to optimize the representation of the data by considering token pair probabilities.

What are the challenges of using BPE for tokenizing languages like Japanese or Chinese?

The challenge with using BPE for languages like Japanese or Chinese is that these languages do not use spaces to separate words. This requires a language-dependent pre-tokenizer to split the text into words or subwords before applying BPE.

How does the SentencePiece tokenizer address the issue of languages without spaces?

The SentencePiece tokenizer treats the input text as a stream of characters, including spaces, and applies a merging algorithm similar to BPE. This allows it to handle languages like Japanese and Chinese, which do not have explicit spaces between words.

What is the difference between SentencePiece and unigram tokenization?

SentencePiece uses a merging algorithm to iteratively combine the most frequent token pairs, while unigram tokenization starts with a large set of tokens and gradually trims them down to form a smaller vocabulary.

What role does the underscore play in SentencePiece tokenization?

In SentencePiece tokenization, the underscore is used as a placeholder for spaces. This allows the tokenizer to reconstruct the original sentence by concatenating the tokens and replacing the underscores with spaces.