Tokenization in LLMs: From Characters to Byte Pair Encoding
Implement BPE tokenization to efficiently encode text for LLM training.
Implement BPE tokenization in your LLM training pipeline to reduce sequence length and improve efficiency.
Summary
Tokenization is the first step in building large language models, and several approaches exist: character‑based, byte‑based, word‑based, and Byte Pair Encoding (BPE). Character‑based tokenization is simple but inefficient, while byte‑based tokenization uses a small vocabulary but leads to long sequences and high compute cost. Word‑based tokenization captures semantic units but creates an unbounded vocabulary and relies on an UNK token. BPE merges frequent byte pairs iteratively, learning statistics from the corpus and handling unknown words gracefully.
BPE, originally a 1994 compression algorithm, became the backbone for models like GPT‑2 and beyond, reducing sequence length and improving training efficiency. It is now the standard tokenization method for modern LLMs, balancing efficiency, robustness, and adaptability to diverse data sets.
Key changes
- Character‑based tokenization maps each character to a code point but is inefficient
- Byte‑based tokenization uses a 0‑256 vocabulary, leading to long sequences
- Word‑based tokenization creates an unbounded vocabulary and uses an UNK token
- BPE merges frequent byte pairs into new tokens iteratively
- BPE learns statistics from the corpus and handles unknown words gracefully
- BPE was originally a 1994 compression algorithm adopted by GPT‑2
- BPE reduces sequence length and improves training efficiency
- BPE is the standard backbone for modern LLMs