metadata
license: mit
multilinguality: multilingual
task_categories:
- multiple-choice
pretty_name: Tokenization Robustness
tags:
- tokenization
- robustness
dataset_info:
- config_name: tokenizer_robustness_completion_stem_canonical
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- config_name: tokenizer_robustness_completion_stem_character_deletion
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- config_name: tokenizer_robustness_completion_stem_double_struck
data_files:
- split: test
path: tokenizer_robustness_completion_stem_double_struck/test-*
- config_name: tokenizer_robustness_completion_stem_enclosed_characters
data_files:
- split: test
path: tokenizer_robustness_completion_stem_enclosed_characters/test-*
- config_name: tokenizer_robustness_completion_stem_equivalent_expressions
data_files:
- split: test
path: tokenizer_robustness_completion_stem_equivalent_expressions/test-*
- config_name: tokenizer_robustness_completion_stem_fullwidth_characters
data_files:
- split: test
path: tokenizer_robustness_completion_stem_fullwidth_characters/test-*
- config_name: tokenizer_robustness_completion_stem_latex
data_files:
- split: test
path: tokenizer_robustness_completion_stem_latex/test-*
- config_name: tokenizer_robustness_completion_stem_morpheme_separation
data_files:
- split: test
path: tokenizer_robustness_completion_stem_morpheme_separation/test-*
- config_name: tokenizer_robustness_completion_stem_scripted_text
data_files:
- split: test
path: tokenizer_robustness_completion_stem_scripted_text/test-*
- config_name: tokenizer_robustness_completion_stem_space_removal
data_files:
- split: test
path: tokenizer_robustness_completion_stem_space_removal/test-*
- config_name: tokenizer_robustness_completion_stem_spelled_out
data_files:
- split: test
path: tokenizer_robustness_completion_stem_spelled_out/test-*
- config_name: tokenizer_robustness_completion_stem_strikethrough
data_files:
- split: test
path: tokenizer_robustness_completion_stem_strikethrough/test-*
- config_name: tokenizer_robustness_completion_stem_superscript_subscript
data_files:
- split: test
path: tokenizer_robustness_completion_stem_superscript_subscript/test-*
- config_name: tokenizer_robustness_completion_stem_typographical_errors
data_files:
- split: test
path: tokenizer_robustness_completion_stem_typographical_errors/test-*
- config_name: tokenizer_robustness_completion_stem_unicode_formatting
data_files:
- split: test
path: tokenizer_robustness_completion_stem_unicode_formatting/test-*
- config_name: tokenizer_robustness_completion_stem_unusual_formatting
data_files:
- split: test
path: tokenizer_robustness_completion_stem_unusual_formatting/test-*
- config_name: tokenizer_robustness_completion_stem_upside_down_rotated
data_files:
- split: test
path: tokenizer_robustness_completion_stem_upside_down_rotated/test-*
language:
- en
size_categories:
- n<1K
Dataset Card for Tokenization Robustness
TokSuite Benchmark (STEM Collection)
Dataset Description
This dataset is the STEM subset of the TokSuite benchmark, designed to evaluate how tokenizer choice affects model behavior under realistic formatting, notation, and surface-form perturbations in technical text. TokSuite includes specialized benchmarks for mathematics and STEM, with the STEM subset containing 44 canonical technical questions paired with a diverse set of targeted perturbations.
- Curated by: R3 Research Team
- License: MIT License
Dataset Summary
TokSuite addresses a fundamental challenge in language model research: understanding how tokenization choices impact model behavior in isolation, independent of architecture, training data, or optimization.
The STEM subset specifically measures model performance on technical and scientific questions under perturbations that commonly arise in real-world STEM communication, including Unicode formatting, mathematical notation, LaTeX representations, spacing changes, and visual styling variants.
Key Features:
- 44 canonical STEM questions covering science, mathematics, engineering, and technical reasoning
- A wide range of formatting- and notation-based perturbations reflecting real-world technical text
- Parallel structure with other TokSuite benchmark subsets
- Designed for controlled robustness evaluation with high baseline accuracy
Supported Tasks
- Multiple-Choice Question Answering: Text completion format with 4 answer choices
- Tokenizer Robustness Evaluation: Measuring performance degradation under surface-form and formatting perturbations
- Technical Text Understanding: Evaluating model robustness on STEM-style content
Languages
The dataset is primarily in English (en), with variations expressed through symbolic, typographic, and Unicode transformations rather than natural-language translation.
Dataset Structure
Data Fields
| Field | Type | Description |
|---|---|---|
question |
string |
The STEM question text |
choices |
list[string] |
4 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 |
subcategories |
string |
Perturbation category |
lang |
string |
Language code (en) |
second_lang |
string |
Optional plain-text or alternative representation |
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 (raw tokens) |
trimmed_cos_sim_to_canonical |
dict[string, float] |
Cosine similarity after token normalization |
token_counts |
dict[string, integer] |
Token counts per tokenizer |
Dataset Creation
Curation Rationale
This dataset was created to:
- Systematically evaluate how different tokenization strategies handle STEM-style text
- Measure robustness to formatting, notation, and Unicode-based perturbations
- Isolate tokenizer effects from semantic reasoning difficulty
- Provide standardized benchmarks for technical text robustness analysis
The questions were intentionally designed to be conceptually straightforward, ensuring high canonical accuracy and enabling clean measurement of performance degradation due solely to perturbations.
Source Data
Data Collection and Processing
- Canonical Questions: 44 STEM questions authored in clean, standard technical English
- Perturbations: Each question was transformed using targeted surface-form and formatting variations
- Validation: Model-in-the-loop filtering ensured canonical questions are answerable with high accuracy
Perturbation Categories
Each perturbation preserves the underlying semantic intent of the canonical STEM question while modifying its surface form, notation, or formatting to stress tokenizer behavior. All perturbations are paired with the same canonical question and differ only in representation.
1. Canonical
Clean, standard technical English with conventional notation, spacing, and formatting. This serves as the reference condition for evaluating robustness.
2. Character Deletion
Removes one or more characters from technical terms, symbols, or variables (e.g., markup → markp). These deletions are subtle but often catastrophic for subword tokenization, especially in STEM terminology.
3. Colloquial
Rewrites the question using more informal or descriptive language while preserving technical meaning. This tests robustness to register changes without altering core content.
4. Compounds
Alters compound technical terms by merging or restructuring components (e.g., removing separators or introducing fused forms), changing token boundaries and segmentation behavior.
5. Diacriticized Styling
Introduces decorative or combining diacritics applied to characters in technical text. These perturbations preserve visual similarity but change Unicode code points and normalization behavior.
6. Double-Struck Characters
Replaces standard Latin characters with mathematical double-struck Unicode forms (e.g., R → ℝ, Z → ℤ), commonly used in mathematical notation.
7. Enclosed Characters
Substitutes alphanumeric characters with enclosed Unicode variants (e.g., A → Ⓐ, 1 → ①), which are visually similar but tokenized very differently.
8. Equivalent Expressions
Rewrites the same STEM concept using an alternative but semantically equivalent formulation, such as paraphrasing definitions or reordering explanatory clauses.
9. Fullwidth Characters
Uses fullwidth Unicode forms (e.g., A → Ａ, 1 → １) instead of standard ASCII characters, altering byte-level and subword tokenization.
10. LaTeX
Represents mathematical expressions or symbols using LaTeX-style notation (e.g., $x^2$, $N_2$, \frac{a}{b}), reflecting common technical writing practices.
11. Morpheme Separation
Artificially splits technical terms into smaller morpheme-like units, increasing sequence length and disrupting learned subword patterns.
12. Scripted Text
Uses scripted or calligraphic Unicode variants of characters (e.g., 𝒜𝒷𝒸) in place of standard Latin letters, stressing visual–semantic mismatch handling.
13. Space Removal
Removes or alters whitespace that is normally meaningful in technical text, such as between variables, units, or multi-word terms.
14. Spelled-Out
Replaces numerals, symbols, or abbreviated technical forms with fully spelled-out textual equivalents (e.g., 2 → two, H2O → water molecule).
15. Strikethrough
Applies strikethrough, combining characters or formatting marks to portions of text, preserving content but introducing visual and Unicode noise.
16. Superscript / Subscript
Uses Unicode superscript and subscript characters (e.g., x², N₂) instead of linear text representations, which often fragment tokenization.
17. Typographical Errors
Introduces realistic typos such as missing letters, duplicated characters, or minor corruptions common in fast technical writing.
18. Unicode Formatting
Applies Unicode formatting characters that affect text rendering or directionality while leaving the visible content largely unchanged.
19. Unusual Formatting
Introduces nonstandard layout, punctuation, or visual formatting patterns that are uncommon but realistic in technical documents.
20. Upside-Down / Rotated
Uses visually rotated or upside-down Unicode characters that resemble standard characters but differ at the code-point level.
Considerations for Using the Data
Social Impact of Dataset
This dataset supports the development of more robust language models for technical and scientific domains, improving reliability in education, research, and engineering applications.
Discussion of Biases
- Domain focus: Emphasizes STEM-style technical text rather than natural language discourse
- Formatting-centric: Perturbations focus on surface form, not conceptual difficulty
- English-centric: Uses English technical text, though many perturbations are language-agnostic
- Question simplicity: Designed for robustness evaluation rather than deep problem-solving
Other Known Limitations
- Evaluation-only dataset (no training split)
- Multiple-choice format
- Limited question count per perturbation
- Results may differ for long-form or open-ended STEM reasoning
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}, booktitle={Preprint}, year={2026}, arxiv={https://arxiv.org/abs/2512.20757}, url={TBD} }
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