Byte-Pair Encoding (BPE)

Byte-Pair Encoding (BPE) is a subword tokenization algorithm that iteratively merges the most frequent pairs of consecutive bytes or characters to create an optimal vocabulary for neural language models. This data-driven approach begins with individual characters and progressively combines frequent character pairs into larger subword units, balancing vocabulary size with representation efficiency. BPE enables models to handle out-of-vocabulary words by decomposing them into known subword components, improving generalization across diverse languages and domains. The algorithm operates through statistical frequency analysis, creating vocabularies that capture common morphological patterns while maintaining computational tractability. Modern implementations of BPE support multilingual tokenization, domain adaptation, and vocabulary size optimization to enhance model performance. This technique serves as the foundation for many state-of-the-art language models, providing robust text representation that handles rare words, proper nouns, and technical terminology effectively across various natural language processing applications.