What is the fundamental purpose of Byte-Pair Encoding (BPE) in the context of tokenization?

The fundamental purpose of Byte-Pair Encoding (BPE) in tokenization is to create a vocabulary that balances the need to represent common words efficiently while also being able to handle rare or unseen words (out-of-vocabulary words). Tokenization is the process of breaking down text into smaller units, called tokens, that can be processed by a model. Traditional tokenization methods often struggle with rare words. For example, if a word doesn't appear frequently in the training data, it might be treated as an unknown token. BPE addresses this issue by starting with individual characters as tokens and iteratively merging the most frequent pairs of tokens into new tokens. This process continues until a desired vocabulary size is reached. As a result, common words are represented by single tokens, while rare words are broken down into smaller, more frequent subword units. For instance, the word 'unbelievable' might be broken down into 'un', 'believe', and 'able'. This allows the model to understand and process words it has never seen before by combining familiar subword units, improving overall performance and reducing the number of unknown tokens.