Overview
Modern language models use subword tokenization to convert text into numbers that neural networks can process. This seemingly technical choice has massive real-world implications - the wrong tokenization strategy can cost companies $500K+ annually in API fees and severely limit model performance.
The Breakdown
- Tokenization pipeline uses five stages - normalizer, pre-tokenizer, model, post-processor, and decoder - to systematically convert text into numerical tokens that neural networks can understand
- Three tokenization approaches exist: character-level creates tiny vocabularies but extremely long sequences, word-level creates short sequences but massive vocabularies, and subword-level balances both by breaking words into meaningful pieces
- Subword methods include BPE (Byte Pair Encoding), WordPiece, and Unigram - each using different algorithms to identify the optimal word fragments for a given language or domain
- Poor tokenization choices create massive downstream costs - inefficient tokenizers can turn 10-token sentences into 50-token sequences, multiplying API costs and training compute requirements
- Custom tokenizers can be built using tools like HuggingFace’s tokenizers library - allowing optimization for specific domains where generic tokenizers perform poorly