Byte Pair Encoding (BPE) is a text tokenization method that splits words into subword units based on frequency. It balances between whole-word and character-level representation, enabling AI models to process rare and compound words efficiently without a massive vocabulary size.
BPE starts with individual characters as tokens and iteratively merges the most frequent adjacent pairs into single tokens. This process continues until a set vocabulary size is reached, creating a compact set of subword tokens that cover common patterns and rare word parts alike.
For AI product managers, BPE improves language model performance by reducing out-of-vocabulary issues and memory footprint. It lowers inference latency and storage needs, enabling scalable, cost-effective deployments. This enhances user experience with better text understanding and faster responses across diverse languages and domains.