An Information-Theoretic Perspective on LLM Tokenizers

2026-01-14

AI Summary

This work addresses the unclear trade-offs among compression efficiency, structural inductive bias, and cross-domain robustness in large language model tokenizers. Viewing tokenization through an information-theoretic lens as structured compression, the authors propose a variant of Byte Pair Encoding (BPE) integrated with principles from compressed sensing and introduce metrics such as channel capacity utilization. They systematically analyze how vocabulary size and training data volume influence text entropy distribution and contextual predictability. Experimental results reveal that while increasing training data enhances token diversity, it simultaneously strengthens contextual predictability. The proposed framework effectively quantifies tokenizer performance, offering both theoretical grounding and practical guidance for designing general-purpose, compression-oriented tokenization strategies and downstream modeling.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Information Theory
• Data Mining & Knowledge Management: Data Compression

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large language model (LLM) tokenizers act as structured compressors: by mapping text to discrete token sequences, they determine token count (and thus compute and context usage) and the statistical structure seen by downstream models. Despite their central role in LLM pipelines, the link between tokenization, compression efficiency and induced structure is not well understood. We empirically demonstrate that tokenizer training scale redistributes entropy: as training data grows, the token stream becomes more diverse in aggregate (higher unigram entropy) yet markedly more predictable in-context (lower higher-order conditional entropies), indicating that tokenization absorbs substantial short-range regularity although these gains degrade under train-test domain mismatch. To ground these observations, we first benchmark i) pretrained GPT-family tokenizers as black-box compressors across various domains, and ii) learned tokenizers across configurations spanning vocabulary size, training scale, and domain. Next, we study tokenization as a transform for universal compression and introduce a compression-aware BPE variant. Finally, we adopt a channel lens and introduce capacity-utilization metrics to analyze tokenizer behaviour and outline implications for downstream modeling. Put together, our results expose various trade-offs between compression, induced structure, and robustness under domain shift, and motivate principled, compression-aware tokenizer design.

Problem

• tokenization
• compression efficiency
• statistical structure
• domain shift
• large language models

Innovation

• information theory
• tokenizer design
• compression-aware BPE
• entropy analysis
• capacity utilization