Writing-System-Level Tokenizer Adaptation for Byte-Level BPE
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Computer Science > Computation and Language
arXiv:2608.00582 (cs)
[Submitted on 1 Aug 2026]
Title:Writing-System-Level Tokenizer Adaptation for Byte-Level BPE
Authors:Bohdan Didenko (Lviv Polytechnic National University)
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Abstract:Pretrained byte-level BPE tokenizers can segment underrepresented languages inefficiently. Replacing a tokenizer changes the meaning of nearly every token ID, while vocabulary expansion enlarges the model's embedding and output matrices. We study post-hoc adaptation that keeps the model-vocabulary size fixed and preserves most existing token-to-ID assignments as a construction-time compatibility property. Directly transferring tokens from a language-specific tokenizer does not guarantee derivability through the target BPE merge graph: an inserted entry can conflict with the target's greedy merge ranks. We formalize this failure as the merge ordering problem and introduce BPE-guided insertion, which builds each transferred token through a target-reachable decomposition. Our pipeline uses script-aware row selection to limit collateral fragmentation, reconstructs target-script byte-level prerequisites, and applies guided insertion to maintain merge-graph reachability. On Ukrainian adaptations of Nemotron and GPT-OSS, it reduces token counts by 33.5% and 36.6%, keeps changes on English and the evaluated four-language European aggregate within 0.05%, and retains 78.5%/77.3% of original model-vocabulary rows at the same IDs. Constraint-matched global and frequency-based removal achieve similar Ukrainian compression but increase English/European token counts by 0.7-2.2%; fresh same-size retraining compresses Ukrainian slightly more but retains effectively no same-ID rows and increases English token counts by 7.6-8.6%. The reallocation increases token counts on the evaluated three-language Cyrillic micro-aggregate by 6.7%/10.1%. Structural audits find all 28,134/45,398 inserted BPE nodes reachable under ordinary rank-ordered merging and no retained same-ID model-vocabulary entry newly broken. We release all tokenizers and code.
| Comments: | 15 pages. Accepted for poster presentation at the Second Tokenization Workshop (TokShop) at COLM 2026 (non-archival) |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2608.00582 [cs.CL] |
| (or arXiv:2608.00582v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2608.00582
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled Writing-System-Level Tokenizer Adaptation for Byte-Level BPE, by Bohdan Didenko (Lviv Polytechnic National University)
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