Tokenisation via Convex Relaxations
Tokenisation is an integral part of the current NLP pipeline. Current tokenisation algorithms such as BPE and Unigram are greedy algorithms -- they make locally optimal decisions without considering the resulting vocabulary as a whole. We instead formulate tokeniser construction as a linear program and solve it using convex optimisation tools, yielding a new algorithm we call ConvexTok. We find ConvexTok consistently improves intrinsic tokenisation metrics and the bits-per-byte (BpB) achieved by language models; it also improves downstream task performance, but less consistently. Furthermore, ConvexTok allows the user to certify how far their tokeniser is from optimal, with respect to a c...
| Authors | Jan Tempus, Philip Whittington, Craig W. Schmidt et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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