---
license: mit
multilinguality: multilingual
task_categories:
- multiple-choice
pretty_name: Tokenization Robustness
tags:
- tokenization
- robustness
dataset_info:
- config_name: tokenizer_robustness_completion_stem_canonical
features:
- name: question
dtype: string
- name: choices
list: string
- name: answer
dtype: int64
- name: answer_label
dtype: string
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dtype: string
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- name: notes
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dtype: string
- name: set_id
dtype: string
- name: variation_id
dtype: string
- name: question_general_category
dtype: string
- name: vanilla_cos_sim_to_canonical
struct:
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- name: tiktoken/gpt-4o
dtype: float64
- name: tokenmonster/englishcode-32000-consistent-v1
dtype: float64
- name: trimmed_cos_sim_to_canonical
struct:
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dtype: float64
- name: tokenmonster/englishcode-32000-consistent-v1
dtype: float64
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dtype: int64
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- name: tiktoken/gpt-4o
dtype: int64
- name: tokenmonster/englishcode-32000-consistent-v1
dtype: int64
splits:
- name: test
num_bytes: 23517
num_examples: 44
download_size: 32406
dataset_size: 23517
- config_name: tokenizer_robustness_completion_stem_character_deletion
features:
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splits:
- name: test
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num_examples: 41
download_size: 40680
dataset_size: 22723
- config_name: tokenizer_robustness_completion_stem_colloquial
features:
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splits:
- name: test
num_bytes: 7820
num_examples: 15
download_size: 32313
dataset_size: 7820
- config_name: tokenizer_robustness_completion_stem_compounds
features:
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dataset_size: 566
- config_name: tokenizer_robustness_completion_stem_diacriticized_styling
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dataset_size: 32623
- config_name: tokenizer_robustness_completion_stem_double_struck
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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:
1. Systematically evaluate how different tokenization strategies handle **STEM-style text**
2. Measure robustness to **formatting, notation, and Unicode-based perturbations**
3. Isolate tokenizer effects from semantic reasoning difficulty
4. 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](TBD)
### 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.
---