--- license: mit multilinguality: multilingual task_categories: - multiple-choice pretty_name: Tokenization Robustness tags: - multilingual - tokenization - robustness dataset_info: - config_name: tokenizer_robustness_completion_chinese_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: category 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 splits: - name: test num_bytes: 8225 num_examples: 40 download_size: 9396 dataset_size: 8225 - config_name: tokenizer_robustness_completion_chinese_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: category 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 splits: - name: test num_bytes: 8136 num_examples: 40 download_size: 8261 dataset_size: 8136 - config_name: tokenizer_robustness_completion_chinese_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: category 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 splits: - name: test num_bytes: 7442 num_examples: 39 download_size: 8111 dataset_size: 7442 - config_name: tokenizer_robustness_completion_chinese_equivalent_expressions 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: category 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 splits: - name: test num_bytes: 7907 num_examples: 40 download_size: 8383 dataset_size: 7907 - config_name: tokenizer_robustness_completion_chinese_keyboard_proximity_errors 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: category 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 splits: - name: test num_bytes: 7340 num_examples: 40 download_size: 8251 dataset_size: 7340 - config_name: tokenizer_robustness_completion_chinese_ocr_errors 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: category 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 splits: - name: test num_bytes: 8441 num_examples: 40 download_size: 8307 dataset_size: 8441 - config_name: tokenizer_robustness_completion_chinese_optional_diacritics 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: category 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 splits: - name: test num_bytes: 10200 num_examples: 40 download_size: 8835 dataset_size: 10200 - config_name: tokenizer_robustness_completion_chinese_partially_romanized 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: category 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 splits: - name: test num_bytes: 7680 num_examples: 40 download_size: 8217 dataset_size: 7680 - config_name: tokenizer_robustness_completion_chinese_romanization 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: category 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 splits: - name: test num_bytes: 7859 num_examples: 40 download_size: 8285 dataset_size: 7859 - config_name: tokenizer_robustness_completion_chinese_space_removal 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: category 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 splits: - name: test num_bytes: 10554 num_examples: 40 download_size: 8618 dataset_size: 10554 - config_name: tokenizer_robustness_completion_chinese_spelled_out 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: category 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 splits: - name: test num_bytes: 2583 num_examples: 13 download_size: 6308 dataset_size: 2583 - config_name: tokenizer_robustness_completion_chinese_traditional 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: category 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 splits: - name: test num_bytes: 6125 num_examples: 33 download_size: 7768 dataset_size: 6125 - config_name: >- tokenizer_robustness_completion_chinese_word_spacing_zero-width_characters_extra_space 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: category 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 splits: - name: test num_bytes: 8831 num_examples: 40 download_size: 8368 dataset_size: 8831 configs: - config_name: tokenizer_robustness_completion_chinese_canonical data_files: - split: test path: tokenizer_robustness_completion_chinese_canonical/test-* - config_name: tokenizer_robustness_completion_chinese_code_language_script_switching data_files: - split: test path: >- tokenizer_robustness_completion_chinese_code_language_script_switching/test-* - config_name: tokenizer_robustness_completion_chinese_colloquial data_files: - split: test path: tokenizer_robustness_completion_chinese_colloquial/test-* - config_name: tokenizer_robustness_completion_chinese_equivalent_expressions data_files: - split: test path: tokenizer_robustness_completion_chinese_equivalent_expressions/test-* - config_name: tokenizer_robustness_completion_chinese_keyboard_proximity_errors data_files: - split: test path: tokenizer_robustness_completion_chinese_keyboard_proximity_errors/test-* - config_name: tokenizer_robustness_completion_chinese_ocr_errors data_files: - split: test path: tokenizer_robustness_completion_chinese_ocr_errors/test-* - config_name: tokenizer_robustness_completion_chinese_optional_diacritics data_files: - split: test path: tokenizer_robustness_completion_chinese_optional_diacritics/test-* - config_name: tokenizer_robustness_completion_chinese_partially_romanized data_files: - split: test path: tokenizer_robustness_completion_chinese_partially_romanized/test-* - config_name: tokenizer_robustness_completion_chinese_romanization data_files: - split: test path: tokenizer_robustness_completion_chinese_romanization/test-* - config_name: tokenizer_robustness_completion_chinese_space_removal data_files: - split: test path: tokenizer_robustness_completion_chinese_space_removal/test-* - config_name: tokenizer_robustness_completion_chinese_spelled_out data_files: - split: test path: tokenizer_robustness_completion_chinese_spelled_out/test-* - config_name: tokenizer_robustness_completion_chinese_traditional data_files: - split: test path: tokenizer_robustness_completion_chinese_traditional/test-* - config_name: >- tokenizer_robustness_completion_chinese_word_spacing_zero-width_characters_extra_space data_files: - split: test path: >- tokenizer_robustness_completion_chinese_word_spacing_zero-width_characters_extra_space/test-* language: - en - zh size_categories: - n<1K --- # Dataset Card for Tokenization Robustness TokSuite Logo # TokSuite Benchmark (Chinese 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 Chinese language multiple-choice text completion questions with various real-world perturbations that test tokenizer robustness. - **Curated by:** R3 Research Team - **Language(s):** Chinese (It) - **License:** MIT License ### Dataset Summary TokSuite addresses a fundamental challenge in language model research: understanding how tokenization choices impact model behavior in isolation. The Chinese 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 Chinese - Parallel structure with TokSuite benchmark (available in English, Turkish, Farsi, Italian) - 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 Chinese text understanding ### Languages The dataset contains text in Chinese (language code: `zho_Hans` / `zh`). ## Dataset Structure ### Data Fields | Field | Type | Description | |-------|------|-------------| | `question` | `string` | The question text in Chinese | | `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: 1. Systematically evaluate how different tokenization strategies handle Chinese 2. Measure robustness against real-world text perturbations specific to Chinese 3. Support research into the impact of tokenization on language model behavior 4. Provide standardized benchmarks for Chinese 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 Chinese speakers translated questions - **Perturbations**: Each question underwent targeted perturbations designed to reflect Chinese characteristics - **Validation**: Model-in-the-loop process ensured high baseline accuracy #### Perturbation Categories 1. **Canonical** The baseline Chinese text written in standard, well-formed Simplified Chinese with no perturbations. This serves as the reference condition for evaluating the impact of all other perturbations. 2. **Code / Language / Script Switching** Mixes Chinese with English words, phrases, or symbols within the same sentence, reflecting real-world bilingual usage and code-switching commonly seen in technical or online contexts. 3. **Colloquial** Rewrites sentences using informal or conversational Chinese expressions, including spoken-style phrasing that differs from standard written Chinese while preserving meaning. 4. **Equivalent Expressions** Replaces canonical phrases with alternative Chinese expressions that convey the same meaning using different words or constructions, isolating tokenizer sensitivity to paraphrasing. 5. **Keyboard Proximity Errors** Introduces character-level errors caused by adjacent key presses in pinyin-based input methods, simulating realistic typing mistakes during Chinese text entry. 6. **OCR Errors** Introduces character substitutions, deletions, or confusions commonly produced by optical character recognition systems, especially for visually similar Chinese characters. 7. **Optional Diacritics** Adds or removes optional diacritic markers (e.g., tone marks in pinyin annotations when present), testing tokenizer robustness to auxiliary pronunciation cues. 8. **Partially Romanized** Mixes Chinese characters with romanized (pinyin or Latin-script) representations for some words or phrases, reflecting hybrid writing styles used in informal digital text. 9. **Romanization** Fully converts Chinese text into romanized form (e.g., pinyin), replacing characters with Latin-script equivalents while preserving pronunciation and meaning. 10. **Space Removal** Removes spaces that may appear between Chinese characters or between Chinese and Latin text, stressing tokenizer assumptions about whitespace usage. 11. **Spelled-Out Forms** Replaces numerals, symbols, or compact expressions with fully spelled-out Chinese equivalents, increasing sequence length and altering token boundaries. 12. **Traditional** Converts Simplified Chinese characters into their Traditional Chinese counterparts, preserving semantics while changing Unicode character forms. 13. **Word Spacing, Zero-Width Characters, Extra Space** Manipulates spacing by inserting extra spaces, removing expected spaces, or adding invisible zero-width characters, stressing tokenizer handling of segmentation and Unicode normalization. #### Who are the source data producers? Native Chinese 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 Chinese text processing. #### Who are the annotators? Native Chinese 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 Chinese speakers by enabling better understanding of tokenization challenges and supporting more robust multilingual models. ### Discussion of Biases - **Language variety** The dataset uses Standard Chinese (Mandarin) and may not fully represent regional or dialectal variations. - **Script focus:** Simplified Chinese is used as the primary script; Traditional Chinese and romanized forms (pinyin) are included as perturbations. - **Domain coverage:** Questions focus on general knowledge and may not represent domain-specific Chinese 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: ```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}, 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. ---
**Part of the [TokSuite Project](TBD)** *Understanding Tokenization's Role in Language Model Behavior*