Universal Byte-Level Encoding: UTF-8/UTF-16 Routing to Reduce Cross-Script Token-Budget Disparities

arXiv:2610.01984 · cs.CL, cs.LG · Submitted 2026-10-01 · Read on arXiv

cs.CL, cs.LG

Submitted: 2026-10-01

Updated: 2026-10-02

Code: https://github.com/meta-llama/llama-models

Terminology

• Tokenization — 44× in this paper · explained in 2 episodes
• Benchmark — 40× in this paper · explained in 2 episodes
• Core — 26× in this paper · explained in 2 episodes
• Accuracy — 16× in this paper · explained in 1 episode
• Robustness — 13× in this paper · explained in 5 episodes
• Transformer — 10× in this paper · explained in 2 episodes
• Decode — 7× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 7× in this paper
• Fidelity — 5× in this paper · explained in 2 episodes
• Universal Byte-Level Encoding — 5× in this paper
• Ablation — 4× in this paper · explained in 1 episode
• Aggregation — 4× in this paper · explained in 1 episode
• Perplexity — 4× in this paper · explained in 3 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Additivity — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Basic Multilingual Plane — 2× in this paper
• Benchmarking — 2× in this paper · explained in 3 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Transformer Architecture — 2× in this paper · explained in 2 episodes
• Automatic Speech Recognition (ASR) — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Low-Resource Languages — 1× in this paper · explained in 4 episodes
• Systematic Bias — 1× in this paper · explained in 3 episodes
• UTF-16 Private Use Area — 1× in this paper

Sources

• One Tokenizer To Rule Them All: Emergent Language Plasticity via Multilingual Tokenizers
• Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs
• Stable LM 2 1.6B Technical Report
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
• Reducing Tokenization Premiums for Low-Resource Languages
• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• Separate Before You Compress: The WWHO Tokenization Architecture
• The Script Tax: Measuring Tokenization-Driven Efficiency and Latency Disparities in Multilingual Language Models
• The Llama 3 Herd of Models
• Bolmo: Byteifying the Next Generation of Language Models
• Bit-level BPE: Below the byte boundary
• Qwen2.5 Technical Report
• MUTANT: A Recipe for Multilingual Tokenizer Design
• The Roots of Performance Disparity in Multilingual Language Models: Intrinsic Modeling Difficulty or Design Choices?
• Fast Transformer Decoding: One Write-Head is All You Need
• The Art of Breaking Words: Rethinking Multilingual Tokenizer Design
• TinyLlama: An Open-Source Small Language Model

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