How Tokenizers in Large Language Models Work: A Deep Dive
Introduction Tokenizers are the unsung heroes of large language models (LLMs), converting raw text into numerical sequences that models can process. Without tokenization, LLMs couldn’t interpret human language, as they operate solely on numbers.[1][4][5] This comprehensive guide explores how tokenizers work, focusing on Byte Pair Encoding (BPE)—the dominant method in modern LLMs like GPT series—while covering fundamentals, algorithms, challenges, and practical implications.[3][5] Why Tokenization Matters in LLMs Tokens are the fundamental units—“atoms”—of LLMs. Everything from input processing to output generation happens in tokens.[3][5] Tokenization breaks text into discrete components, assigns each a unique ID, and maps it to an embedding vector for the model.[1][2][4] ...