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 ---
license: mit
multilinguality: multilingual
task_categories:
- multiple-choice
pretty_name: Tokenization Robustness
tags:
- multilingual
- tokenization
configs:
- config_name: farsi_tokenizer_robustness_canonical
data_files:
- split: test
path: farsi_tokenizer_robustness_cannonical/test-*
- config_name: farsi_tokenizer_robustness_code_language_script_switching
data_files:
- split: test
path: farsi_tokenizer_robustness_code_language_script_switching/test-*
- config_name: farsi_tokenizer_robustness_colloquial
data_files:
- split: test
path: farsi_tokenizer_robustness_colloquial/test-*
- config_name: farsi_tokenizer_robustness_diacritics_presence_absence
data_files:
- split: test
path: farsi_tokenizer_robustness_diacritics_presence_absence/test-*
- config_name: farsi_tokenizer_robustness_keyboard_proximity_errors
data_files:
- split: test
path: farsi_tokenizer_robustness_keyboard_proximity_errors/test-*
- config_name: farsi_tokenizer_robustness_romanization
data_files:
- split: test
path: farsi_tokenizer_robustness_romanization/test-*
- config_name: farsi_tokenizer_robustness_word_reordering
data_files:
- split: test
path: farsi_tokenizer_robustness_word_reordering/test-*
- config_name: farsi_tokenizer_robustness_word_spacing_zero-width_characters_extra_space
data_files:
- split: test
path: >-
farsi_tokenizer_robustness_word_spacing_zero-width_characters_extra_space/test-*
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language:
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size_categories:
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---
<!-- 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 (Farsi 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 Farsi (Persian) language multiple-choice text completion questions with various real-world perturbations that test tokenizer robustness.
- **Curated by:** R3 Research Team
- **Language(s):** Farsi/Persian (fa)
- **License:** MIT License
### Dataset Summary
TokSuite addresses a fundamental challenge in language model research: understanding how tokenization choices impact model behavior in isolation. The Farsi subset specifically measures model performance on canonical questions and various perturbations including orthographic variations, diacritics, morphological challenges, and noise commonly encountered when processing Farsi text.
**Key Features:**
- 45 canonical questions covering general knowledge, geography, science, and language understanding
- Multiple perturbation types reflecting real-world text variations in Farsi
- Parallel structure with TokSuite benchmark (available in English, Turkish, Italian, Chinese)
- Native speaker curation ensuring linguistic authenticity
### 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 Farsi text understanding
### Languages
The dataset contains text in Farsi (Persian) written in Arabic script (language code: `pes_Arab` / `fa`).
## Dataset Structure
### Data Instances
An example from the dataset:
```json
{
"question": "رنگ آسمان",
"choices": ["آبی است", "قرمز است", "سبز است", "زرد است"],
"answer": 0,
"answer_label": "A",
"split": "test",
"subcategories": "Canonical",
"lang": "pes_Arab",
"second_lang": "The color of the sky is",
"coding_lang": "",
"notes": "The color of the sky is",
"id": "301",
"set_id": 301.0,
"variation_id": 1.0
}
```
### Data Fields
| Field | Type | Description |
|-------|------|-------------|
| `question` | `string` | The question text in Farsi (Persian Arabic script) |
| `choices` | `list[string]` | Four multiple-choice answer options in Farsi |
| `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 (pes_Arab = Persian/Farsi in Arabic script) |
| `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 300-344) |
| `variation_id` | `float64` | Variation number within a question set |
## Dataset Creation
### Curation Rationale
This dataset was created to:
1. Systematically evaluate how different tokenization strategies handle Farsi text
2. Measure robustness against real-world text perturbations specific to Farsi language
3. Support research into tokenization's impact on language model behavior
4. Provide standardized benchmarks for Farsi language models
The questions were designed to be straightforward with high baseline accuracy, allowing researchers to cleanly measure performance degradation when perturbations are applied.
### Source Data
#### Data Collection and Processing
- **Canonical Questions**: 40 baseline questions in English were created covering general knowledge topics
- **Translation**: Native Farsi speakers translated questions to Persian
- **Perturbations**: Each question underwent targeted perturbations designed to reflect morphological and orthographic characteristics of Farsi
- **Validation**: Model-in-the-loop process ensured high baseline accuracy across 14 different tokenizers
#### Perturbation Categories
1. **Canonical**
The baseline/standard form of Farsi text without any modifications, used as the reference point for comparing other perturbations.
2. **Code Language Script Switching**
Mixing Farsi with English language (code-switching), randomly switching between Farsi and English words mid-sentence.
3. **Colloquial**
Using informal, conversational Farsi instead of formal written language, including slang, dialectal variations, and everyday speech patterns.
4. **Diacritics Presence/Absence**
Adding diacritical marks (vowel markings and other pronunciation indicators) that can be optionally included in Farsi text, which affects how words are read.
5. **Keyboard Proximity Errors**
Typos caused by hitting adjacent keys on a keyboard, simulating common typing mistakes where the wrong character is typed due to finger placement.
6. **Romanization**
Converting Farsi text to Finglish—writing Farsi words using English/Latin letters instead of Persian script.
7. **Word Reordering**
Changing the order of words in sentences, testing whether tokenizers can handle different syntactic arrangements.
8. **Word Spacing, Zero-Width Characters, Extra Space**
Manipulating spacing between words by adding extra spaces, removing spaces, or inserting invisible zero-width characters that affect how text is segmented.
#### Model Performance Comparison
| model_name | canonical | arabic_keyboard_for_farsi | code_language_script_switching | colloquial | dialects | equivalent_expressions | keyboard_proximity_errors | number_romanization | optional_diacritics | romanization | spelled_out | word_spacing_zero-width_characters_extra_space |
|:-------------|------------:|----------------------------:|---------------------------------:|-------------:|-----------:|-------------------------:|----------------------------:|----------------------:|----------------------:|---------------:|--------------:|-------------------------------------------------:|
| Aya | 0.78 | 0.346 | 0.717 | 0.661 | 0.529 | 0.607 | 0.409 | 0.744 | 0.438 | 0.346 | 0.458 | 0.557 |
| BLOOM | 0.775 | 0.448 | 0.77 | 0.6 | 0.505 | 0.675 | 0.571 | 0.669 | 0.505 | 0.276 | 0.542 | 0.589 |
| ByT5 | 0.769 | 0.478 | 0.719 | 0.591 | 0.531 | 0.616 | 0.527 | 0.568 | 0.446 | 0.28 | 0.337 | 0.476 |
| Comma | 0.79 | 0.471 | 0.66 | 0.652 | 0.523 | 0.66 | 0.503 | 0.617 | 0.457 | 0.449 | 0.291 | 0.484 |
| GPT-2 | 0.78 | 0.569 | 0.672 | 0.739 | 0.545 | 0.66 | 0.616 | 0.498 | 0.436 | 0.298 | 0.449 | 0.573 |
| GPT-4o | 0.75 | 0.406 | 0.744 | 0.669 | 0.504 | 0.744 | 0.588 | 0.752 | 0.375 | 0.306 | 0.466 | 0.544 |
| Gemma-2 | 0.75 | 0.375 | 0.569 | 0.688 | 0.475 | 0.712 | 0.544 | 0.44 | 0.431 | 0.425 | 0.446 | 0.5 |
| Llama-3.2 | 0.743 | 0.355 | 0.688 | 0.587 | 0.55 | 0.675 | 0.499 | 0.907 | 0.291 | 0.304 | 0.429 | 0.46 |
| Phi-3 | 0.82 | 0.48 | 0.675 | 0.593 | 0.501 | 0.63 | 0.542 | 0.555 | 0.493 | 0.328 | 0.469 | 0.593 |
| Qwen-3 | 0.857 | 0.428 | 0.643 | 0.545 | 0.541 | 0.59 | 0.534 | 0.644 | 0.455 | 0.252 | 0.384 | 0.473 |
| Tekken | 0.842 | 0.481 | 0.743 | 0.594 | 0.51 | 0.697 | 0.561 | 0.853 | 0.449 | 0.318 | 0.522 | 0.547 |
| TokenMonster | 0.714 | 0.533 | 0.622 | 0.671 | 0.521 | 0.61 | 0.542 | 0.728 | 0.523 | 0.352 | 0.318 | 0.519 |
| XGLM | 0.757 | 0.499 | 0.669 | 0.558 | 0.522 | 0.706 | 0.539 | 0.644 | 0.462 | 0.297 | 0.415 | 0.559 |
| mBERT | 0.746 | 0.377 | 0.678 | 0.678 | 0.508 | 0.659 | 0.585 | 0.402 | 0.547 | 0.414 | 0.296 | 0.659 |
#### Who are the source data producers?
Native Farsi speakers curated and validated all questions and perturbations. The TokSuite research team at R3 designed the overall benchmark framework.
### Annotations
#### Annotation process
Questions were manually created and translated by native speakers. Each perturbation was carefully designed to reflect authentic variations encountered in real-world Farsi text processing.
#### Who are the annotators?
Native Farsi speakers with expertise in linguistics and NLP, working as part of the TokSuite project.
### Personal and Sensitive Information
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
This dataset contributes to improving language technology for Farsi speakers by:
- Enabling better understanding of tokenization challenges in Persian
- Supporting development of more robust multilingual models
- Providing standardized evaluation for Farsi NLP research
### Discussion of Biases
- **Language variety**: The dataset uses Modern Standard Persian and may not fully represent dialectal variations
- **Script focus**: Only Arabic script is used; romanized versions are included as perturbations
- **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 Farsi'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
### 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:
```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}
}
```
**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 through the paper submission system.
---
<div align="center">
**Part of the [TokSuite Project](TBD)**
*Understanding Tokenization's Role in Language Model Behavior*
</div>