An Overview of Byte Pair Encoding (BPE)

Created in September 18, 2025

2025

Byte Pair Encoding (BPE) is a simple but powerful data compression technique that has become a cornerstone of modern Natural Language Processing (NLP). Originally developed for data compression, it has been adapted to create tokenizers that efficiently handle large and diverse vocabularies in language models.

How BPE Works

The BPE algorithm works by iteratively merging the most frequent pair of adjacent bytes (or characters) in a corpus into a new, single token. This process is repeated for a set number of merges, resulting in a vocabulary that represents common character sequences and whole words as single tokens.

Here’s a simplified breakdown of the process:

  1. Initialization: The initial vocabulary consists of all individual characters present in the training corpus.
  2. Iteration: For a predetermined number of steps:
    • Find the pair of adjacent tokens that appears most frequently in the corpus.
    • Merge this pair into a new, single token.
    • Add the new token to the vocabulary.
    • Replace all occurrences of the pair in the corpus with the new token.

This iterative merging allows the model to learn subword units, which is particularly effective for handling rare words, misspellings, and morphologically rich languages. Instead of assigning an “unknown” token to words not seen during training, BPE can break them down into known subword units.

BPE in Modern NLP

Many state-of-the-art language models, including GPT-3, use BPE or a similar subword tokenization algorithm (like WordPiece or SentencePiece). By using subword units, these models can:

  • Handle any word: The vocabulary is not limited to a fixed set of words.
  • Reduce vocabulary size: Compared to using whole words, subword tokenization creates a much more manageable vocabulary size.
  • Share information: Morphologically related words (e.g., “run”, “running”, “ran”) share subword tokens, which can help the model generalize better.

This post serves as a high-level introduction to BPE. In future posts, we may dive deeper into the implementation details and compare it with other tokenization strategies.