Functionalizer cuts vocab size and boosts code modeling with lossless tokenization
The paper introduces the Functionalizer, a pre‑tokenization framework that separates orthographic and structural word variations into a stream of opcodes and a canonical base token. By encoding transformations such as casing, diacritics and character repetition in the Unicode Private Use Area, the method preserves every original form without inflating the vocabulary. Tests on six…
Key points
- Functionalizer reduces vocabulary size by up to 16% across diverse corpora while remaining lossless
- Improves code syntax validity and character perplexity for 25M‑parameter GPT‑2 models
- Compresses code sequences but can increase length of natural‑language prose
When applied to 25‑million‑parameter GPT‑2‑scale models, the Functionalizer markedly improves code syntax validity and lowers character‑level perplexity, while keeping prose quality comparable to standard tokenizers. The authors argue that functional decomposition offers a promising path toward more compact, structurally aware language models and suggest further large‑scale validation.
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