← 研踪 PaperTrailarXiv:2610.01984 · v3
Universal Byte-Level Encoding: UTF-8/UTF-16 Routing to Reduce Cross-Script Token-Budget Disparities
首次提交 2026-10-02 00:26
版本更新 2026-11-08 11:06
雷达观测 2026-11-10 12:16
摘要
Byte-level byte-pair encoding (BBPE) tokenizers are attractive for multilingual large language models because they cover all Unicode text. In UTF-8-based BBPE, however, many scripts start from a higher fallback cost than English: when no learned merges apply, a multibyte character requires multiple byte-derived symbols. We call this worst-case pre-merge cost the encoding floor. A higher floor can increase token counts and per-request cost while shrinking usable context. Changing the whole text encoding can reduce this gap but make already-efficient English spans more expensive in mixed-script text. Thus, we propose Universal Byte-Level Encoding (UBE), a dual-alphabet tokenizer that keeps 1-2-byte UTF-8 characters in UTF-8 and routes 3-4-byte characters through UTF-16. This lowers the encoding floor for 3-byte Basic Multilingual Plane characters, used by many scripts that need more tokens than English, without raising it for any character. UBE changes only the symbols presented to byte-pair encoding, and a Unicode 17 audit confirms exact round trips for every scalar value and every input in three official test suites. UBE also composes with other boundary policies and morphology-based encodings. Across intrinsic evaluations, UBE lowers dispersion in English-normalized token-count ratios (Gini 6-14% lower on FLORES-200 at 16K-64K), reducing cross-lingual token-budget disparity. In multilingual language models, UBE matches BBPE's quality. In the main multilingual settings, UBE reduces token counts most where they are highest and also lowers English counts, yielding more usable context and faster prompt processing in content-matched benchmarks. Byte-level models benefit directly: UBE shortens their inputs by 29-33% in all 39 of the FLORES-200 languages written mostly in 3-byte characters (1.4-1.5 times as much text per context window), lowers byte-level Gini by 28%, and lengthens no input.
作者备注：NeurIPS 2026 camera-ready. Code: https://github.com/Samsung/UBE
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v3 · 2026-11-08 11:06 · 最新观测版本
Universal Byte-Level Encoding: UTF-8/UTF-16 Routing to Reduce Cross-Script Token-Budget Disparities
Byte-level byte-pair encoding (BBPE) tokenizers are attractive for multilingual large language models because they cover all Unicode text. In UTF-8-based BBPE, however, many scripts start from a higher fallback cost than English: when no learned merges apply, a multibyte character requires multiple byte-derived symbols. We call this worst-case pre-merge cost the encoding floor. A higher floor can increase token counts and per-request cost while shrinking usable context. Changing the whole text encoding can reduce this gap but make already-efficient English spans more expensive in mixed-script text. Thus, we propose Universal Byte-Level Encoding (UBE), a dual-alphabet tokenizer that keeps 1-2-byte UTF-8 characters in UTF-8 and routes 3-4-byte characters through UTF-16. This lowers the encoding floor for 3-byte Basic Multilingual Plane characters, used by many scripts that need more tokens than English, without raising it for any character. UBE changes only the symbols presented to byte-pair encoding, and a Unicode 17 audit confirms exact round trips for every scalar value and every input in three official test suites. UBE also composes with other boundary policies and morphology-based encodings. Across intrinsic evaluations, UBE lowers dispersion in English-normalized token-count ratios (Gini 6-14% lower on FLORES-200 at 16K-64K), reducing cross-lingual token-budget disparity. In multilingual language models, UBE matches BBPE's quality. In the main multilingual settings, UBE reduces token counts most where they are highest and also lowers English counts, yielding more usable context and faster prompt processing in content-matched benchmarks. Byte-level models benefit directly: UBE shortens their inputs by 29-33% in all 39 of the FLORES-200 languages written mostly in 3-byte characters (1.4-1.5 times as much text per context window), lowers byte-level Gini by 28%, and lengthens no input.
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