A Pilot Study of Autocompleting Tokenizers
Modern input methods routinely rely on autocomplete to omit information that can be recovered from local context. Inspired by these autocomplete-assisted writing systems, we investigate whether Transformer inputs can be compressed in a similar manner. Byte-level tokenization offers a simple and language-independent alternative to subword tokenization, but its longer input sequences typically result in increased computational cost and reduced model quality. We propose a compression scheme that employs a lightweight autoregressive byte language model to identify and remove bytes that are easily predictable from their surrounding context before Transformer processing. The resulting compresse...
| Authors | Samuel Wexler, Mark Hopkins |
|---|---|
| Primary category | cs.CL |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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