Byte Pair Encoding: Compressing Text for LLMs
Think of Byte Pair Encoding (BPE) as creating custom abbreviations for common letter pairs to compress text. It repeatedly finds the most frequent pair, like 'th', and merges it into a new token. LLMs use this to build vocabularies of common sub-word units, helping them understand rare words. The main footgun is that the final vocabulary size is fixed; choosing the wrong size can hurt model performance and efficiency.
Think of Byte Pair Encoding (BPE) as an algorithm that creates custom abbreviations for common letter pairs to compress text. It starts with a vocabulary of individual characters and iteratively finds the most frequent pair of adjacent tokens (e.g., 't' + 'h') in the corpus, merging them into a single new token ('th'). LLMs use a modified version of this to build their vocabularies. This allows them to represent common words and sub-words efficiently, helping them handle rare or misspelled words gracefully. The main footgun is that the final vocabulary size is a fixed hyperparameter. If set too small, even common words get split into many meaningless pieces; too large, and the model's embedding matrix becomes bloated and slow.
Read the original → Wikipedia: Byte pair encoding
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- #tokenization
- #data-compression
- #nlp
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