Datasets:
metadata
license: mit
multilinguality: multilingual
task_categories:
- multiple-choice
pretty_name: Tokenization Robustness
tags:
- multilingual
- tokenization
- robustness
dataset_info:
- config_name: tokenizer_robustness_completion_turkish_canonical
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: categories
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: vanilla_cos_sim_to_canonical
struct:
- name: CohereLabs/aya-expanse-8b
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:
- name: CohereLabs/aya-expanse-8b
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: 22368
num_examples: 40
download_size: 33264
dataset_size: 22368
- config_name: tokenizer_robustness_completion_turkish_code_language_script_switching
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: categories
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: 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: google-bert/bert-base-multilingual-cased
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- name: google/byt5-small
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- name: gpt2
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- name: meta-llama/Llama-3.2-1B
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- name: microsoft/Phi-3-mini-4k-instruct
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- name: tiktoken/gpt-4o
dtype: float64
- name: tokenmonster/englishcode-32000-consistent-v1
dtype: float64
- name: token_counts
struct:
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- name: Qwen/Qwen3-8B
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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: 26317
num_examples: 46
download_size: 41715
dataset_size: 26317
- config_name: tokenizer_robustness_completion_turkish_colloquial
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: categories
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: vanilla_cos_sim_to_canonical
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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: tiktoken/gpt-4o
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- name: tokenmonster/englishcode-32000-consistent-v1
dtype: float64
- name: token_counts
struct:
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- name: Qwen/Qwen3-8B
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- name: gpt2
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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: mistralai/tekken
dtype: int64
- name: tiktoken/gpt-4o
dtype: int64
- name: tokenmonster/englishcode-32000-consistent-v1
dtype: int64
splits:
- name: test
num_bytes: 20785
num_examples: 37
download_size: 39952
dataset_size: 20785
- config_name: tokenizer_robustness_completion_turkish_date_formats
features:
- name: question
dtype: string
- name: choices
list: string
- name: answer
dtype: int64
- name: answer_label
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- name: split
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- name: lang
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dtype: string
- name: vanilla_cos_sim_to_canonical
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- name: tiktoken/gpt-4o
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- 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: tokenmonster/englishcode-32000-consistent-v1
dtype: int64
splits:
- name: test
num_bytes: 6084
num_examples: 11
download_size: 30448
dataset_size: 6084
- config_name: tokenizer_robustness_completion_turkish_derivations
features:
- name: question
dtype: string
- name: choices
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- name: tiktoken/gpt-4o
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- name: tokenmonster/englishcode-32000-consistent-v1
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struct:
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- name: tokenmonster/englishcode-32000-consistent-v1
dtype: int64
splits:
- name: test
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num_examples: 41
download_size: 41279
dataset_size: 22780
- config_name: tokenizer_robustness_completion_turkish_dialects
features:
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splits:
- name: test
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num_examples: 36
download_size: 38838
dataset_size: 20322
- config_name: tokenizer_robustness_completion_turkish_english_keyboard
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configs:
- config_name: tokenizer_robustness_completion_turkish_canonical
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_canonical/test-*
- config_name: tokenizer_robustness_completion_turkish_code_language_script_switching
data_files:
- split: test
path: >-
tokenizer_robustness_completion_turkish_code_language_script_switching/test-*
- config_name: tokenizer_robustness_completion_turkish_colloquial
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_colloquial/test-*
- config_name: tokenizer_robustness_completion_turkish_date_formats
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_date_formats/test-*
- config_name: tokenizer_robustness_completion_turkish_derivations
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_derivations/test-*
- config_name: tokenizer_robustness_completion_turkish_dialects
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_dialects/test-*
- config_name: tokenizer_robustness_completion_turkish_english_keyboard
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_english_keyboard/test-*
- config_name: tokenizer_robustness_completion_turkish_equivalent_expressions
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_equivalent_expressions/test-*
- config_name: tokenizer_robustness_completion_turkish_grammatical_errors
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_grammatical_errors/test-*
- config_name: tokenizer_robustness_completion_turkish_inflections
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_inflections/test-*
- config_name: tokenizer_robustness_completion_turkish_keyboard_proximity_errors
data_files:
- split: test
path: >-
tokenizer_robustness_completion_turkish_keyboard_proximity_errors/test-*
- config_name: tokenizer_robustness_completion_turkish_orthographic_errors
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_orthographic_errors/test-*
- config_name: tokenizer_robustness_completion_turkish_similar_words
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_similar_words/test-*
- config_name: tokenizer_robustness_completion_turkish_spelled_out
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_spelled_out/test-*
- config_name: tokenizer_robustness_completion_turkish_typographical_errors
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_typographical_errors/test-*
- config_name: tokenizer_robustness_completion_turkish_web_search_query
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_web_search_query/test-*
- config_name: tokenizer_robustness_completion_turkish_word_reordering
data_files:
- split: test
path: tokenizer_robustness_completion_turkish_word_reordering/test-*
language:
- tr
- en
size_categories:
- n<1K
Dataset Card for Tokenization Robustness
TokSuite Benchmark (Turkish 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 Turkish language multiple-choice text completion questions with various real-world perturbations that test tokenizer robustness.
- Curated by: R3 Research Team
- Language(s): Turkish (Tr)
- License: MIT License
Dataset Summary
TokSuite addresses a fundamental challenge in language model research: understanding how tokenization choices impact model behavior in isolation. The Turkish subset specifically measures model performance on canonical questions and various perturbations.
Key Features:
- 40 canonical questions covering general knowledge, geography, science, and language understanding
- Multiple perturbation types reflecting real-world text variations in Turkish
- Parallel structure with TokSuite benchmark (available in English, Italian, Farsi, 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 Turkish text understanding
Languages
The dataset contains text in Turkish (language code: tur_Latn / tr).
Dataset Structure
Data Fields
| Field | Type | Description |
|---|---|---|
question |
string |
The question text in Turkish |
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 |
second_lang |
string |
English translation or description of the question |
notes |
string |
Additional context about the question or 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 scores to canonical form (raw tokens) |
trimmed_cos_sim_to_canonical |
dict[string, float] |
Cosine similarity scores after token normalization |
token_counts |
dict[string, integer] |
Number of tokens produced per tokenizer |
Dataset Creation
Curation Rationale
This dataset was created to:
- Systematically evaluate how different tokenization strategies handle Turkish
- Measure robustness against real-world text perturbations specific to Turkish
- Support research into the impact of tokenization on language model behavior
- Provide standardized benchmarks for Turkish 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 created in English
- Translation: Native Turkish speakers translated questions
- Perturbations: Each question underwent targeted perturbations designed to reflect Turkish characteristics
- Validation: Model-in-the-loop process ensured high baseline accuracy
Perturbation Categories
Canonical The baseline Turkish text written in standard, grammatically correct Turkish with no perturbations. This serves as the reference condition for evaluating the impact of all other perturbations.
Abbreviations Introduces common Turkish abbreviations and shortened forms (e.g.,
Dr.,Prof.,vb.,sn.), testing tokenizer robustness to compressed lexical forms.Capitalization Alters capitalization patterns by randomly capitalizing, lowercasing, or mixing case within words and sentences, simulating informal writing or casing errors.
Code / Language / Script Switching Mixes Turkish with English words or phrases within the same sentence, reflecting real-world code-switching common in technical, academic, or online Turkish text.
Contractions Applies contracted or fused forms common in informal Turkish writing (e.g., dropped vowels or merged suffix boundaries), stressing tokenizer handling of agglutinative morphology.
Date Formats Varies date representations (e.g.,
12.03.2022,12 Mart 2022,03/12/22), testing sensitivity to formatting and punctuation variation.Dialects Introduces regional Turkish dialectal or colloquial variants that preserve meaning but differ lexically or morphologically from Standard Turkish.
English Keyboard Simulates Turkish text typed on an English keyboard, leading to missing or substituted Turkish-specific characters (e.g.,
cokinstead ofçok,saglikinstead ofsağlık).Grammatical Errors Injects plausible grammatical mistakes such as incorrect suffix usage, agreement errors, or case marking issues, reflecting non-standard or learner Turkish.
Keyboard Proximity Errors Introduces typos caused by pressing adjacent keys on a keyboard, simulating realistic typing errors without intentionally changing word choice.
Numerical Formats Varies numeric representations (e.g.,
1.000vs.1000, comma vs. period usage for decimals), testing tokenizer sensitivity to locale-specific number formatting.Orthographic Errors Applies spelling mistakes that violate standard Turkish orthography (e.g., incorrect consonant usage or misspelled suffixes) while remaining plausible to native readers.
Phonetic Spelling Replaces words with spellings based on pronunciation rather than standard orthography, reflecting informal or speech-inspired Turkish writing.
Plausible Diacritics Errors Introduces missing, incorrect, or substituted diacritics (e.g.,
svs.ş,gvs.ğ,ivs.ı), testing tokenizer sensitivity to Turkish-specific characters.Similar Words Substitutes words with closely related or easily confusable alternatives (e.g., near-synonyms or minimal lexical contrasts), preserving sentence plausibility.
Spelled-Out Forms Replaces numerals, abbreviations, or symbols with fully spelled-out Turkish equivalents, increasing sequence length and altering token boundaries.
Typographical Errors Introduces general typographical mistakes such as duplicated letters, missing characters, or minor corruption commonly found in fast or careless typing.
Web Search Query Rewrites questions in the style of Turkish web search queries, using keyword-heavy phrasing, omitted function words, and informal structure typical of search engine inputs.
Who are the source data producers?
Native Turkish 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 Turkish text processing.
Who are the annotators?
Native Turkish 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 Turkish speakers by enabling better understanding of tokenization challenges and supporting more robust multilingual models.
Discussion of Biases
- Language variety: he dataset uses Standard Turkish (Türkiye Türkçesi) and may not fully represent regional or dialectal variations.
- Script focus: Only the Latin script is used; Turkish-specific diacritics and keyboard-related variations are included as perturbations.
- Domain coverage: Questions focus on general knowledge and may not represent domain-specific Turkish language use.
- Question simplicity: Designed for high baseline accuracy, which may not reflect real-world task complexity.
Other Known Limitations
- Relatively small dataset size (evaluation-only)
- Multiple-choice format
- Language-specific perturbations
- Results may differ at larger model 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:
@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