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metadata
license: mit
multilinguality: multilingual
task_categories:
  - multiple-choice
pretty_name: Tokenization Robustness Math
tags:
  - tokenization
  - mathematics
dataset_info:
  - config_name: tokenizer_robustness_completion_math_canonical
    features:
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        dtype: string
      - name: choices
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      - name: subcategories
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      - name: category
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        dtype: string
      - name: set_id
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          - name: tiktoken/gpt-4o
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          - name: tokenmonster/englishcode-32000-consistent-v1
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        num_examples: 21
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  - config_name: tokenizer_robustness_completion_math_chinese
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    splits:
      - name: test
        num_bytes: 11147
        num_examples: 21
    download_size: 34445
    dataset_size: 11147
  - config_name: tokenizer_robustness_completion_math_decorative_unicode
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      - name: test
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    download_size: 34660
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  - config_name: tokenizer_robustness_completion_math_farsi
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  - config_name: tokenizer_robustness_completion_math_italian
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  - config_name: tokenizer_robustness_completion_math_latex
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configs:
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    data_files:
      - split: test
        path: tokenizer_robustness_completion_math_canonical/test-*
  - config_name: tokenizer_robustness_completion_math_chinese
    data_files:
      - split: test
        path: tokenizer_robustness_completion_math_chinese/test-*
  - config_name: tokenizer_robustness_completion_math_decorative_unicode
    data_files:
      - split: test
        path: tokenizer_robustness_completion_math_decorative_unicode/test-*
  - config_name: tokenizer_robustness_completion_math_farsi
    data_files:
      - split: test
        path: tokenizer_robustness_completion_math_farsi/test-*
  - config_name: tokenizer_robustness_completion_math_italian
    data_files:
      - split: test
        path: tokenizer_robustness_completion_math_italian/test-*
  - config_name: tokenizer_robustness_completion_math_latex
    data_files:
      - split: test
        path: tokenizer_robustness_completion_math_latex/test-*
  - config_name: tokenizer_robustness_completion_math_space_removal
    data_files:
      - split: test
        path: tokenizer_robustness_completion_math_space_removal/test-*
  - config_name: tokenizer_robustness_completion_math_spelled_out
    data_files:
      - split: test
        path: tokenizer_robustness_completion_math_spelled_out/test-*
  - config_name: tokenizer_robustness_completion_math_turkish
    data_files:
      - split: test
        path: tokenizer_robustness_completion_math_turkish/test-*
language:
  - en
  - fa
  - zh
  - it
  - tr
size_categories:
  - n<1K

Dataset Card for Tokenization Robustness (Math)

TokSuite Logo

TokSuite Benchmark (Math Collection)

Dataset Description

This dataset is part of TokSuite, a comprehensive benchmark designed to measure how different tokenization strategies affect language model behavior under controlled conditions.

This specific subset focuses on mathematical text completion, containing multiple-choice math questions with a variety of surface-form perturbations that stress tokenizer handling of numbers, symbols, formatting, scripts, and mathematical notation.

  • Curated by: R3 Research Team
  • Domain: Mathematics
  • License: MIT License

Dataset Summary

TokSuite isolates the impact of tokenization by holding model architecture, training data, training budget, and initialization constant, varying only the tokenizer.

The Math benchmark evaluates performance on:

  • A canonical mathematical formulation
  • Multiple perturbed variants that preserve mathematical meaning while altering surface representation

These perturbations reflect realistic variation in how mathematical expressions are written, formatted, localized, and queried in practice.

Key Features:

  • 21 canonical math questions with unambiguous answers
  • Perturbations targeting notation, symbols, scripts, and formatting
  • Parallel structure with TokSuite language benchmarks
  • Designed for evaluation, not training

Supported Tasks

  • Multiple-Choice Math Question Answering
  • Tokenizer Robustness Evaluation
  • Symbolic and Numerical Text Processing

Dataset Structure

Data Fields

Field Type Description
question string Mathematical question text
choices list[string] Multiple-choice answer options
answer int64 Index of the correct answer
answer_label string Letter label of the correct answer
split string Dataset split identifier (all entries are test)
subcategories string Perturbation category
lang string Domain identifier (math)
notes string Additional context about the perturbation
id string Unique question identifier
set_id float64 Question set grouping identifier
variation_id float64 Variation number within a question set
vanilla_cos_sim_to_canonical dict[string, float] Cosine similarity to canonical form using raw token sequences
trimmed_cos_sim_to_canonical dict[string, float] Cosine similarity after token normalization
token_counts dict[string, int] Number of tokens produced per tokenizer

Dataset Creation

Curation Rationale

This dataset was created to:

  1. Systematically evaluate tokenizer robustness on mathematical notation and structure
  2. Measure sensitivity to changes in formatting, symbols, scripts, and numeric representation
  3. Isolate tokenization effects from mathematical reasoning difficulty
  4. Provide standardized benchmarks for math-focused language models

Canonical questions are intentionally simple and high-accuracy, allowing researchers to attribute performance degradation to tokenization rather than reasoning complexity.

Source Data

  • Canonical math questions were manually authored
  • Each question was perturbed while preserving mathematical equivalence
  • Canonical accuracy was validated across TokSuite models

Perturbation Categories (Math)

  1. Canonical
    The baseline mathematical text written in a standard, well-formatted form with no perturbations. This serves as the reference condition for evaluating all other perturbations.

  2. Chinese
    Rewrites mathematical text using Chinese characters for numbers, operators, or surrounding descriptions, testing tokenizer robustness to non-Latin scripts in math contexts.

  3. Decorative Unicode
    Replaces standard mathematical symbols with visually similar decorative or stylized Unicode characters (e.g., fancy numerals or operators), stressing Unicode normalization and symbol handling.

  4. Farsi
    Introduces Persian (Farsi) numerals or script elements into mathematical expressions, testing tokenizer robustness to right-to-left scripts and cross-script numeric representations.

  5. Italian
    Rewrites textual components of math problems in Italian while preserving the same mathematical structure and solution.

  6. LaTeX
    Encodes mathematical expressions using LaTeX-style syntax (e.g., \frac, ^, _), stressing tokenizer handling of markup-heavy mathematical notation.

  7. Space Removal
    Removes or alters spacing within mathematical expressions and surrounding text, stressing tokenizer assumptions about whitespace in math contexts.

  8. Spelled-Out Forms
    Replaces numerals or symbols with fully spelled-out textual equivalents (e.g., numbers written as words), increasing sequence length and altering token boundaries.

  9. Turkish
    Rewrites textual components of math problems in Turkish while preserving the underlying mathematical meaning.

Considerations for Using the Data

  • Language variety: The dataset uses standard mathematical notation and English-language math phrasing, and may not represent informal or pedagogical math language.
  • Script focus: Mathematical expressions are primarily written using ASCII and standard Unicode; LaTeX, decorative Unicode, and non-Latin scripts are included as perturbations.
  • Domain coverage: Questions focus on general mathematics and may not represent highly specialized or advanced mathematical domains.
  • Question simplicity: Designed for high baseline accuracy, which may not reflect real-world mathematical task complexity.

Additional Information

Dataset Curators

The dataset was curated by the TokSuite research team at R3.

Licensing Information

MIT License

Citation Information

If you use this dataset in your research, please cite the TokSuite paper:

@inproceedings{toksuite2026,
  title={TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior},
  author={Altıntaş, Gül Sena and Ehghaghi, Malikeh and Lester, Brian and Liu, Fengyuan and Zhao, Wanru and Ciccone, Marco and Raffel, Colin},
  year={2026}
}

Paper: TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior

Contributions

This dataset is part of TokSuite, which includes:

  • 14 language models with identical architectures but different tokenizers
  • Multilingual benchmark datasets (English, Turkish, Italian, Farsi, Chinese)
  • Comprehensive analysis of tokenization's impact on model behavior

Contact

For questions or issues related to this dataset, please refer to the TokSuite project or contact the authors of the paper.

Part of the TokSuite Project

Understanding Tokenization's Role in Language Model Behavior