TL;DR
A subword tokenization algorithm that iteratively merges the most frequent pairs of bytes or characters in a text to construct an optimized vocabulary of tokens for language models.
Byte-Pair Encoding acts as a bridge between character-level and word-level representations. It builds a vocabulary by starting with individual characters and progressively merging the most common adjacent symbol pairs. This allows language models to efficiently handle rare words and morphological variations while maintaining a manageable vocabulary size.
Why this matters for your business
It forms the backbone of tokenization for major transformer architectures, determining how text inputs are parsed and consumed by the neural network.
