TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior (2512.20757v1)

Abstract: Tokenizers provide the fundamental basis through which text is represented and processed by LMs. Despite the importance of tokenization, its role in LM performance and behavior is poorly understood due to the challenge of measuring the impact of tokenization in isolation. To address this need, we present TokSuite, a collection of models and a benchmark that supports research into tokenization's influence on LMs. Specifically, we train fourteen models that use different tokenizers but are otherwise identical using the same architecture, dataset, training budget, and initialization. Additionally, we curate and release a new benchmark that specifically measures model performance subject to real-world perturbations that are likely to influence tokenization. Together, TokSuite allows robust decoupling of the influence of a model's tokenizer, supporting a series of novel findings that elucidate the respective benefits and shortcomings of a wide range of popular tokenizers.

• The paper demonstrates that tokenizer choice significantly alters LLM performance under noise and perturbations, affecting efficiency, accuracy, and robustness.
• It employs a controlled ablation study with identical models differing only in tokenization method to isolate tokenization’s unique effects.
• Findings reveal vulnerabilities in handling Unicode styling and technical notation, underscoring tokenization as a critical factor alongside architecture and data.

TokSuite: Measuring the Impact of Tokenizer Choice on LLM Behavior

Motivation and Context

Text tokenization, the decomposition of text into subword units or characters, is a foundational stage in Transformer-based LLM architectures, affecting model efficiency, representation capacity, and robustness across linguistic phenomena. Although tokenization is as consequential as model architecture and pretraining data in determining final model behavior, its design is often treated as an ancillary decision. The absence of rigorous, controlled studies that isolate the effect of tokenization—distinct from changes in data, random seeds, or hyperparameters—has left substantial gaps in our understanding of how tokenization shapes LLM performance in realistic settings. Addressing this, "TokSuite: Measuring the Impact of Tokenizer Choice on LLM Behavior" (2512.20757) presents a controlled experimental suite and comprehensive benchmark for quantifying the downstream consequences of tokenizer design across a broad range of contemporary algorithms and vocabularies.

Experimental Design: Controlled Tokenizer Ablation

The central methodological contribution of TokSuite is the construction and open release of fourteen Transformer LMs (Llama-3.2-1B scale), each differing only in tokenizer, with identical architectures, parameter initialization (aligned via a super-vocabulary), data, and compute stacks. The selected tokenizers span byte-level, BPE, Unigram, WordPiece, and custom (TokenMonster) approaches, with vocabulary sizes ranging from 259 (ByT5) to 256k (Gemma-2, XGLM). Both monolingual (English-centric) and multilingual tokenizers are included, targeting English, Turkish, Italian, Farsi (Arabic script), and Mandarin Chinese. Crucially, all models are trained on precisely the same multilingual corpus and fixed token budget, ensuring that observed differences reflect only the consequences of segmentation policy.

Figure 1: TokSuite's benchmark covers perturbations that alter tokenization and a spectrum of models differing only by tokenizer; the visualization (left) shows fragmentation of "doctor" under real-world noise across representative tokenizers.

This design enables direct attribution of downstream differences in accuracy, efficiency, and robustness to tokenizer choice, in a manner not possible via post-hoc comparisons between public/pretrained LLMs.

Benchmark Construction: Real-World Perturbations and Multilinguality

To interrogate the brittleness and inductive biases introduced by tokenization, the authors develop a multilingual benchmark comprising approximately 5,000 multiple-choice completions. These are systematically translated and manually perturbed by native speakers for language-specific orthographic, morphological, and typographical phenomena. The suite targets:

• Orthographic variations: OCR artifacts, misspellings, homoglyphs, zero-width/joining characters
• Morphological diversity: contractions, inflections, derivations, compound boundaries
• Register/style and code-switching: slang, colloquial expressions, emoji, keyword-style queries
• Script/diacritic variants: transliteration/romanization (e.g., Pinyin, Finglish), accent omissions/additions, historical spelling
• Noise: random typos, scrambling, deletion, space removal
• Mathematical/STEM notation: LaTeX, Unicode math, ASCII diagrams, systematic chemical nomenclature, romanized numerals
• Unicode stylings: doubled, enclosed, full-width and decorated characters, extreme casing transformations

The test set is uniquely constructed so that all models achieve high (>70%) canonical accuracy, and performance degradations under perturbation can be reliably attributed to tokenization rather than model confusion or lack of knowledge.

Empirical Analysis: Intrinsic and Downstream Effects of Tokenization

Intrinsic Efficiency and Segmentation

Quantitative analysis of subword fertility, parity, and the proportion of continued words (PCW) reveals that, as expected, smaller vocabularies (ByT5, TokenMonster, Phi-3) generate highly segmented sequences, especially in morphologically rich or non-Latin scripts, leading to inefficiency in context consumption. Models like mBERT and XGLM—multilingual, large-vocabulary Unigram models—optimally minimize average tokens-per-word and produce more consistent cross-lingual segmentations.

Figure 2: Distribution of fertility scores demonstrating the segmentation granularity per tokenizer for each language.

However, vocabulary scaling yields diminishing returns: increasing vocabulary size (e.g., Qwen-3, Gemma-2 >150k) does not guarantee efficient or consistent encodings. Token duplication (e.g., multiple stylistic/whitespace-variant tokens for the same semantic item) and incomplete script coverage emerge as systematic issues in large-vocabulary models.

Downstream Performance Under Perturbation

Figure 3: Aggregate accuracy of models on multilingual benchmarks, measuring canonical and perturbed performance.

Key empirical findings include:

• Tokenization robustness is weakly correlated with vocabulary size. TokenMonster, a monolingual English-only tokenizer with 32k vocabulary, achieves the strongest average robustness (lowest mean accuracy drop 0.18), outperforming much larger, putatively more expressive, multilingual tokenizers.
• Noise and script perturbations cause catastrophic segmentation. Non-English perturbations, especially in Farsi and Turkish, amplify brittleness: small script or spacing errors lead to radical fragmentation or [UNK]/byte-fallback token emissions, severely degrading semantic compositionality.
• Character/byte-level models (ByT5) attain high robustness but at a large efficiency cost. ByT5 consistently outperforms subword models on perturbed/rare cases, with almost-perfect consistency across noise, register, or Unicode styling attacks, but exhibits pathological context inefficiency (subword fertility >7 on non-English).
• Structural notation, LaTeX, and ASCII diagrams remain unsolved. All models, including large-vocabulary and subword methods, show >0.3 average drop for STEM/LaTeX perturbations, where trivial whitespace or formatting changes impede parsing.
• No tokenizer is robust to Unicode styling and decorated character attacks. All models attain significant performance drops (>0.53 avg), except XGLM, which achieves some robustness by aggressive NFKC normalization (at the cost of complete loss of stylized/structured data).
• Scaling model capacity brings minimal improvement to robustness. Identical architectures at 1B, 3B, and 7B scales (e.g., Llama-3.2) preserve fragility patterns; scaling-invariant robustness profiles confirm that tokenizer design, not parameter count, is the limiting factor.

  Figure 4: Accuracies on canonical vs. perturbed questions per language, capturing model sensitivities in multilingual deployment.

  

  Figure 5: Bootstrapped distributions of robustness by tokenizer, highlighting significant outliers and mean-centered fragility/robustness clusters categorized by vocabulary size.

Critical Observations by Phenomenon

Diacritics and script variants: Tokenizers trained on undiacritized data (Chinese, Farsi) are extremely brittle under diacritic perturbation—even when such marks clarify ambiguous sequences. BLOOM and TokenMonster show differential robustness due to explicit handling.

Dialect, register, and code-switching: TokenMonster consistently demonstrates minimal performance drops across style, colloquial phrasing, and code-switched segments while multilingual, large-vocabulary models fail on dialectal and register variants.

Mathematics and technical notation: All models exhibit high vulnerability to LaTeX, expanded numerals, and scientific ASCII notation; whitespace, bracing, or formatting changes disrupt downstream performance.

Unicode styling: No contemporary tokenizer (except for aggressive normalizers) can consistently process full-width, doubled, or decorated characters, reflecting practical weaknesses for search, social media, or adversarial use cases.

Statistical Rigor

Bootstrapped estimates and Wilcoxon signed-rank tests validate the significance of all principal findings—the majority of model-to-model performance differences by perturbation exceed one standard deviation and are highly significant ($p < 0.0001$).

Theoretical and Practical Implications

The results empirically validate several theoretical predictions in the tokenization literature:

• Segmentation consistency and robustness trade-off: Granular (byte/character) models maximize robustness at the expense of context window efficiency; subword models are efficient but susceptible to fragmentation and [UNK]/byte-fallback in the face of morphological variance or input noise.
• No universal optimum: Maximizing robustness for one language, domain, or typographical class degrades performance elsewhere due to competing inductive biases embedded in tokenization policy (compression, normalization, OOV handling).
• Tokenization is a dominant factor in LLM deployment fragility: Increased data, model scale, or instruction tuning does not reliably erase architectural bottlenecks or eliminate systemic weaknesses in input encoding, especially outside English.

Outlook and Future Work

The paper clearly demonstrates that tokenization decisions cannot be regarded as inconsequential hyperparameters—they directly condition model robustness, generality, and efficiency. Current trends in aggressive vocabulary scaling offer diminishing returns; custom-designed tokenization algorithms (e.g., TokenMonster's "ungreedy" approach) offer promising alternatives but require further research and real-world evaluation. Record-keeping of tokenizer properties, systematic, language- or domain-specific benchmarking, and the development of robustifying frontends (including normalization, adversarial defense, and byte-level fallbacks) should become standard practice in LLM pipelines.

Avenues for future exploration include the extension of these analyses to code, additional non-Latin scripts, broader linguistic coverage, and the development of hybrid or adaptive tokenization schemes that can dynamically adjust segmentation policy based on input characteristics. Furthermore, integrating findings from controlled tokenizer variation into both LLM distillation and low-resource language adaptation will accelerate robust multilingual LM deployment.

Conclusion

TokSuite establishes that tokenizer design is a first-order control over LLM behavioral boundaries and fragility, equaling or surpassing the effects of data size or model scale under realistic, noisy, multilingual settings. The study's open resources—aligned model suite and benchmark—provide a rigorous foundation for future work on tokenization-aware language modeling and evaluation.

Citation: "TokSuite: Measuring the Impact of Tokenizer Choice on LLM Behavior" (2512.20757).