Update README.md

README.md

@@ -1,12 +1,12 @@
 ---
-license: cc
+license: mit
 multilinguality: multilingual
 task_categories:
 - multiple-choice
 pretty_name: Tokenization Robustness Math
 tags:
-- multilingual
 - tokenization
+- mathematics
 dataset_info:
 - config_name: tokenizer_robustness_completion_math_canonical
   features:
@@ -1161,192 +1161,139 @@ configs:
   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)
 
-# Dataset Card for Tokenization Robustness Math
 <!-- Provide a quick summary of the dataset. -->
 
-<img src="toksuite-logo.png" alt="TokSuite Logo" width="250px" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
-
-# TokSuite Benchmark ({LANGUAGE_NAME} Collection)
+<img src="toksuite-logo.png" alt="TokSuite Logo" width="250px" style="margin-left:auto; margin-right:auto; display:block;"/>
 
+# 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 performance and robustness. This specific subset contains {LANGUAGE_NAME} language multiple-choice text completion questions with various real-world perturbations that test tokenizer robustness.
+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
-- **Language(s):** {LANGUAGE_NAME} ({LANGUAGE_CODE})
-- **License:** MIT License
+- **Curated by:** R3 Research Team  
+- **Domain:** Mathematics  
+- **License:** MIT License  
 
 ### Dataset Summary
 
-TokSuite addresses a fundamental challenge in language model research: understanding how tokenization choices impact model behavior in isolation. The {LANGUAGE_NAME} subset specifically measures model performance on canonical questions and various perturbations including {LIST_KEY_PERTURBATION_TYPES}.
+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:**
-- {NUM_CANONICAL_QUESTIONS} canonical questions covering {TOPIC_AREAS}
-- Multiple perturbation types reflecting real-world text variations in {LANGUAGE_NAME}
-- Parallel structure with TokSuite benchmark (available in English, Turkish, Italian, Chinese, Farsi)
-- Native speaker curation ensuring linguistic authenticity
+- 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 Question Answering**: Text completion format with 4 answer choices
-- **Tokenizer Robustness Evaluation**: Measuring performance degradation under various text perturbations
-- **Multilingual NLP Benchmarking**: Evaluating language models on {LANGUAGE_NAME} text understanding
+- **Multiple-Choice Math Question Answering**
+- **Tokenizer Robustness Evaluation**
+- **Symbolic and Numerical Text Processing**
 
-### Languages
-
-The dataset contains text in {LANGUAGE_NAME} written in {SCRIPT_NAME} (language code: {LANGUAGE_CODE_FULL}).
+---
 
 ## Dataset Structure
 
-### Data Instances
-
-An example from the dataset:
-```json
-{
-  "question": "{EXAMPLE_QUESTION}",
-  "choices": ["{CHOICE_A}", "{CHOICE_B}", "{CHOICE_C}", "{CHOICE_D}"],
-  "answer": {ANSWER_INDEX},
-  "answer_label": "{ANSWER_LABEL}",
-  "split": "test",
-  "subcategories": "{SUBCATEGORY}",
-  "lang": "{LANGUAGE_CODE_FULL}",
-  "second_lang": "{ENGLISH_TRANSLATION}",
-  "coding_lang": "",
-  "notes": "{NOTES}",
-  "id": "{ID}",
-  "set_id": {SET_ID},
-  "variation_id": {VARIATION_ID}
-}
-```
-
 ### Data Fields
 
 | Field | Type | Description |
-|-------|------|-------------|
-| question | string | The question text in {LANGUAGE_NAME} ({SCRIPT_DESCRIPTION}) |
-| choices | list[string] | Four multiple-choice answer options in {LANGUAGE_NAME} |
-| answer | int64 | Index of the correct answer (0-3) |
-| answer_label | string | Letter label of the correct answer (A, B, C, or D) |
-| split | string | Dataset split identifier (all entries are "test") |
-| subcategories | string | Perturbation category |
-| lang | string | Language code ({LANGUAGE_CODE_FULL} = {LANGUAGE_DESCRIPTION}) |
-| second_lang | string | English translation or description of the question |
-| coding_lang | string | Not applicable for this dataset (empty string) |
-| notes | string | Additional context about the question or perturbation type |
-| id | string | Unique question identifier |
-| set_id | float64 | Question set grouping identifier (ranges from {ID_RANGE_START}-{ID_RANGE_END}) |
-| variation_id | float64 | Variation number within a question set |
+|------|------|-------------|
+| `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 how different tokenization strategies handle {LANGUAGE_NAME} text
-2. Measure robustness against real-world text perturbations specific to {LANGUAGE_NAME} language
-3. Support research into tokenization's impact on language model behavior
-4. Provide standardized benchmarks for {LANGUAGE_NAME} language models
+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
 
-The questions were designed to be straightforward with high baseline accuracy, allowing researchers to cleanly measure performance degradation when perturbations are applied.
+Canonical questions are intentionally **simple and high-accuracy**, allowing researchers to attribute performance degradation to tokenization rather than reasoning complexity.
 
 ### Source Data
 
-#### Data Collection and Processing
-
-- **Canonical Questions**: {NUM_BASE_QUESTIONS} baseline questions in English were created covering general knowledge topics
-- **Translation**: Native {LANGUAGE_NAME} speakers translated questions to {LANGUAGE_NAME}
-- **Perturbations**: Each question underwent targeted perturbations designed to reflect {LINGUISTIC_CHARACTERISTICS}
-- **Validation**: Model-in-the-loop process ensured high baseline accuracy across 14 different tokenizers
-
-#### Perturbation Categories
-
-1. **Canonical**
-{DESCRIPTION_OF_CANONICAL}
-
-2. **{PERTURBATION_NAME_1}**
-{DESCRIPTION_1}
+- Canonical math questions were manually authored
+- Each question was perturbed while preserving mathematical equivalence
+- Canonical accuracy was validated across TokSuite models
 
-3. **{PERTURBATION_NAME_2}**
-{DESCRIPTION_2}
-
-4. **{PERTURBATION_NAME_3}**
-{DESCRIPTION_3}
-
-5. **{PERTURBATION_NAME_4}**
-{DESCRIPTION_4}
-
-6. **{PERTURBATION_NAME_5}**
-{DESCRIPTION_5}
-
-7. **{PERTURBATION_NAME_6}**
-{DESCRIPTION_6}
-
-8. **{PERTURBATION_NAME_7}**
-{DESCRIPTION_7}
+---
 
-#### Model Performance Comparison
+## Perturbation Categories (Math)
 
-| model_name   | canonical | {PERTURBATION_COL_1} | {PERTURBATION_COL_2} | {PERTURBATION_COL_3} | {PERTURBATION_COL_4} | {PERTURBATION_COL_5} | {PERTURBATION_COL_6} | {PERTURBATION_COL_7} |
-|:-------------|----------:|---------------------:|---------------------:|---------------------:|---------------------:|---------------------:|---------------------:|---------------------:|
-| Aya          |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
-| BLOOM        |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
-| ByT5         |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
-| Comma        |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
-| GPT-2        |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
-| GPT-4o       |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
-| Gemma-2      |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
-| Llama-3.2    |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
-| Phi-3        |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
-| Qwen-3       |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
-| Tekken       |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
-| TokenMonster |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
-| XGLM         |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
-| mBERT        |     {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |                {VAL} |
+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.
 
-#### Who are the source data producers?
+2. **Chinese**  
+Rewrites mathematical text using Chinese characters for numbers, operators, or surrounding descriptions, testing tokenizer robustness to non-Latin scripts in math contexts.
 
-Native {LANGUAGE_NAME} speakers curated and validated all questions and perturbations. The TokSuite research team at R3 designed the overall benchmark framework.
+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.
 
-### Annotations
+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.
 
-#### Annotation process
+5. **Italian**  
+Rewrites textual components of math problems in Italian while preserving the same mathematical structure and solution.
 
-Questions were manually created and translated by native speakers. Each perturbation was carefully designed to reflect authentic variations encountered in real-world {LANGUAGE_NAME} text processing.
+6. **LaTeX**  
+Encodes mathematical expressions using LaTeX-style syntax (e.g., `\frac`, `^`, `_`), stressing tokenizer handling of markup-heavy mathematical notation.
 
-#### Who are the annotators?
+7. **Space Removal**  
+Removes or alters spacing within mathematical expressions and surrounding text, stressing tokenizer assumptions about whitespace in math contexts.
 
-Native {LANGUAGE_NAME} speakers with expertise in linguistics and NLP, working as part of the TokSuite project.
+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.
 
-### Personal and Sensitive Information
+9. **Turkish**  
+Rewrites textual components of math problems in Turkish while preserving the underlying mathematical meaning.
 
-The dataset contains only general knowledge questions and does not include any personal or sensitive information.
+---
 
 ## Considerations for Using the Data
 
-### Social Impact of Dataset
+- **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.
 
-This dataset contributes to improving language technology for {LANGUAGE_NAME} speakers by:
-- Enabling better understanding of tokenization challenges in {LANGUAGE_NAME}
-- Supporting development of more robust multilingual models
-- Providing standardized evaluation for {LANGUAGE_NAME} NLP research
-
-### Discussion of Biases
-
-- **Language variety**: The dataset uses {STANDARD_VARIETY} and may not fully represent dialectal variations
-- **Script focus**: {SCRIPT_LIMITATIONS_DESCRIPTION}
-- **Domain coverage**: Questions focus on general knowledge and may not represent domain-specific language use
-- **Question simplicity**: Designed for high baseline accuracy, which may not reflect real-world task complexity
-
-### Other Known Limitations
-
-- Relatively small dataset size (designed for evaluation, not training)
-- Focus on multiple-choice format may not capture all aspects of language understanding
-- Perturbations are specific to {LANGUAGE_NAME}'s characteristics and findings may not generalize to all languages
-- Models evaluated were trained at ~1B parameters; results may differ at larger scales
+---
 
 ## Additional Information
 
@@ -1356,21 +1303,19 @@ The dataset was curated by the TokSuite research team at R3.
 
 ### Licensing Information
 
-MIT license 
+MIT License
 
 ### Citation Information
 
 If you use this dataset in your research, please cite the TokSuite paper:
+
 ```bibtex
 @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},
-  url={TBD}
+  year={2026}
 }
-```
-
+```''
 **Paper**: [TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior](TBD)
 
 ### Contributions
@@ -1383,7 +1328,7 @@ This dataset is part of TokSuite, which includes:
 
 ### Contact
 
-For questions or issues related to this dataset, please refer to the TokSuite project or contact the authors through the paper submission system.
+For questions or issues related to this dataset, please refer to the TokSuite project or contact the authors of the paper.
 
 ---