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---
license: mit
multilinguality: multilingual
task_categories:
- multiple-choice
pretty_name: Tokenization Robustness
tags:
- multilingual
- tokenization
- robustness
dataset_info:
- config_name: tokenizer_robustness_completion_general_abbreviations
features:
- name: question
dtype: string
- name: choices
list: string
- name: answer
dtype: int64
- name: answer_label
dtype: string
- name: split
dtype: string
- name: subcategories
dtype: string
- name: lang
dtype: string
- name: second_lang
dtype: string
- name: notes
dtype: string
- name: id
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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dtype: float64
- name: Qwen/Qwen3-8B
dtype: float64
- name: bigscience/bloom
dtype: float64
- name: common-pile/comma-v0.1-1t
dtype: float64
- name: facebook/xglm-564M
dtype: float64
- name: google-bert/bert-base-multilingual-cased
dtype: float64
- name: google/byt5-small
dtype: float64
- name: google/gemma-2-2b
dtype: float64
- name: gpt2
dtype: float64
- name: meta-llama/Llama-3.2-1B
dtype: float64
- name: microsoft/Phi-3-mini-4k-instruct
dtype: float64
- name: mistralai/tekken
dtype: float64
- 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: Qwen/Qwen3-8B
dtype: float64
- name: bigscience/bloom
dtype: float64
- name: common-pile/comma-v0.1-1t
dtype: float64
- name: facebook/xglm-564M
dtype: float64
- name: google-bert/bert-base-multilingual-cased
dtype: float64
- name: google/byt5-small
dtype: float64
- name: google/gemma-2-2b
dtype: float64
- name: gpt2
dtype: float64
- name: meta-llama/Llama-3.2-1B
dtype: float64
- name: microsoft/Phi-3-mini-4k-instruct
dtype: float64
- name: mistralai/tekken
dtype: float64
- name: tiktoken/gpt-4o
dtype: float64
- name: tokenmonster/englishcode-32000-consistent-v1
dtype: float64
- name: token_counts
struct:
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dtype: int64
- name: Qwen/Qwen3-8B
dtype: int64
- name: bigscience/bloom
dtype: int64
- name: common-pile/comma-v0.1-1t
dtype: int64
- name: facebook/xglm-564M
dtype: int64
- name: google-bert/bert-base-multilingual-cased
dtype: int64
- name: google/byt5-small
dtype: int64
- name: google/gemma-2-2b
dtype: int64
- name: gpt2
dtype: int64
- name: meta-llama/Llama-3.2-1B
dtype: int64
- name: microsoft/Phi-3-mini-4k-instruct
dtype: int64
- name: mistralai/tekken
dtype: int64
- name: tiktoken/gpt-4o
dtype: int64
- name: tokenmonster/englishcode-32000-consistent-v1
dtype: int64
splits:
- name: test
num_bytes: 10169
num_examples: 18
download_size: 33425
dataset_size: 10169
- config_name: tokenizer_robustness_completion_general_canonical
features:
- name: question
dtype: string
- name: choices
list: string
- name: answer
dtype: int64
- name: answer_label
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- name: subcategories
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- name: lang
dtype: string
- name: second_lang
dtype: string
- name: notes
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- name: id
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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- name: Qwen/Qwen3-8B
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- name: facebook/xglm-564M
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- name: tiktoken/gpt-4o
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- name: tokenmonster/englishcode-32000-consistent-v1
dtype: float64
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struct:
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- name: Qwen/Qwen3-8B
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- name: meta-llama/Llama-3.2-1B
dtype: int64
- name: microsoft/Phi-3-mini-4k-instruct
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- name: tokenmonster/englishcode-32000-consistent-v1
dtype: int64
splits:
- name: test
num_bytes: 1122
num_examples: 2
download_size: 28358
dataset_size: 1122
- config_name: tokenizer_robustness_completion_general_character_deletion
features:
- name: question
dtype: string
- name: choices
list: string
- name: answer
dtype: int64
- name: answer_label
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- name: notes
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dtype: string
- name: variation_id
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- name: question_general_category
dtype: string
- name: vanilla_cos_sim_to_canonical
struct:
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- name: mistralai/tekken
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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: Qwen/Qwen3-8B
dtype: float64
- name: bigscience/bloom
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- name: facebook/xglm-564M
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- name: meta-llama/Llama-3.2-1B
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- name: tiktoken/gpt-4o
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- name: tokenmonster/englishcode-32000-consistent-v1
dtype: float64
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struct:
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- name: Qwen/Qwen3-8B
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- name: microsoft/Phi-3-mini-4k-instruct
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- name: tokenmonster/englishcode-32000-consistent-v1
dtype: int64
splits:
- name: test
num_bytes: 4498
num_examples: 8
download_size: 30562
dataset_size: 4498
- config_name: tokenizer_robustness_completion_general_currency_symbol
features:
- name: question
dtype: string
- name: choices
list: string
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struct:
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- name: tokenmonster/englishcode-32000-consistent-v1
dtype: int64
splits:
- name: test
num_bytes: 8300
num_examples: 15
download_size: 32265
dataset_size: 8300
- config_name: tokenizer_robustness_completion_general_date_formats
features:
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dtype: int64
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dtype: int64
- name: tokenmonster/englishcode-32000-consistent-v1
dtype: int64
splits:
- name: test
num_bytes: 13058
num_examples: 24
download_size: 34371
dataset_size: 13058
- config_name: tokenizer_robustness_completion_general_unusual_formatting
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dtype: string
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- name: Qwen/Qwen3-8B
dtype: int64
- name: bigscience/bloom
dtype: int64
- name: common-pile/comma-v0.1-1t
dtype: int64
- name: facebook/xglm-564M
dtype: int64
- name: google-bert/bert-base-multilingual-cased
dtype: int64
- name: google/byt5-small
dtype: int64
- name: google/gemma-2-2b
dtype: int64
- name: gpt2
dtype: int64
- name: meta-llama/Llama-3.2-1B
dtype: int64
- name: microsoft/Phi-3-mini-4k-instruct
dtype: int64
- name: mistralai/tekken
dtype: int64
- name: tiktoken/gpt-4o
dtype: int64
- name: tokenmonster/englishcode-32000-consistent-v1
dtype: int64
splits:
- name: test
num_bytes: 580
num_examples: 1
download_size: 27964
dataset_size: 580
configs:
- config_name: tokenizer_robustness_completion_general_abbreviations
data_files:
- split: test
path: tokenizer_robustness_completion_general_abbreviations/test-*
- config_name: tokenizer_robustness_completion_general_canonical
data_files:
- split: test
path: tokenizer_robustness_completion_general_canonical/test-*
- config_name: tokenizer_robustness_completion_general_character_deletion
data_files:
- split: test
path: tokenizer_robustness_completion_general_character_deletion/test-*
- config_name: tokenizer_robustness_completion_general_currency_symbol
data_files:
- split: test
path: tokenizer_robustness_completion_general_currency_symbol/test-*
- config_name: tokenizer_robustness_completion_general_date_formats
data_files:
- split: test
path: tokenizer_robustness_completion_general_date_formats/test-*
- config_name: tokenizer_robustness_completion_general_unusual_formatting
data_files:
- split: test
path: tokenizer_robustness_completion_general_unusual_formatting/test-*
language:
- en
size_categories:
- n<1K
---
# Dataset Card for Tokenization Robustness
<!-- 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 Bonus Benchmarks (General Collection)
This is a **bonus TokSuite dataset** containing a small set of **high-signal examples** that highlight surface-form variations known to affect tokenization robustness. It includes canonical questions alongside perturbations such as abbreviations, character deletion, currency symbols, diverse date formats, and unusual formatting. These examples focus on **tokenization challenges** that commonly arise in real-world text, providing a compact complement to the main TokSuite benchmarks.
## 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.
---
<div align="center">
**Part of the [TokSuite Project](TBD)**
*Understanding Tokenization's Role in Language Model Behavior*
</div>
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