README.md

---
license: mit
multilinguality: multilingual
task_categories:
- multiple-choice
pretty_name: Tokenization Robustness
tags:
- tokenization
- robustness
dataset_info:
- config_name: tokenizer_robustness_completion_stem_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: 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:
    - name: CohereLabs/aya-expanse-8b
      dtype: float64
    - name: Qwen/Qwen3-8B
      dtype: float64
    - name: bigscience/bloom
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    - name: common-pile/comma-v0.1-1t
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    - 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
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    - 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
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    - name: common-pile/comma-v0.1-1t
      dtype: float64
    - name: facebook/xglm-564M
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    - 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
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    - 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: 23517
    num_examples: 44
  download_size: 32406
  dataset_size: 23517
- config_name: tokenizer_robustness_completion_stem_character_deletion
  features:
  - name: question
    dtype: string
  - name: choices
    list: string
  - name: answer
    dtype: int64
  - name: answer_label
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  - name: split
    dtype: string
  - name: subcategories
    dtype: string
  - name: lang
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  - 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
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    - name: Qwen/Qwen3-8B
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    - name: bigscience/bloom
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    - name: common-pile/comma-v0.1-1t
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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: google/gemma-2-2b
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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: mistralai/tekken
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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
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    - name: Qwen/Qwen3-8B
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    - name: bigscience/bloom
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    - name: common-pile/comma-v0.1-1t
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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: google/gemma-2-2b
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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: mistralai/tekken
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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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      dtype: int64
    - name: Qwen/Qwen3-8B
      dtype: int64
    - name: bigscience/bloom
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    - name: common-pile/comma-v0.1-1t
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    - name: facebook/xglm-564M
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    - name: google-bert/bert-base-multilingual-cased
      dtype: int64
    - name: google/byt5-small
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    - 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: 22723
    num_examples: 41
  download_size: 40680
  dataset_size: 22723
- config_name: tokenizer_robustness_completion_stem_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: 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
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    - name: Qwen/Qwen3-8B
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    - name: bigscience/bloom
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    - name: common-pile/comma-v0.1-1t
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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
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    - name: tokenmonster/englishcode-32000-consistent-v1
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  - name: trimmed_cos_sim_to_canonical
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    - name: Qwen/Qwen3-8B
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    - name: bigscience/bloom
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    - name: google/byt5-small
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    - name: gpt2
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    - name: microsoft/Phi-3-mini-4k-instruct
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    - name: mistralai/tekken
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    - name: tiktoken/gpt-4o
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    - name: tokenmonster/englishcode-32000-consistent-v1
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  - name: token_counts
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    - name: Qwen/Qwen3-8B
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    - name: bigscience/bloom
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    - name: common-pile/comma-v0.1-1t
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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: google/gemma-2-2b
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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
      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: 7820
    num_examples: 15
  download_size: 32313
  dataset_size: 7820
- config_name: tokenizer_robustness_completion_stem_compounds
  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: subcategories
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  - name: lang
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  - 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
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    - name: CohereLabs/aya-expanse-8b
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    - name: Qwen/Qwen3-8B
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    - name: bigscience/bloom
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    - name: common-pile/comma-v0.1-1t
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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: meta-llama/Llama-3.2-1B
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    - name: microsoft/Phi-3-mini-4k-instruct
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    - name: Qwen/Qwen3-8B
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    - name: bigscience/bloom
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      dtype: float64
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    - name: tiktoken/gpt-4o
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    - name: tokenmonster/englishcode-32000-consistent-v1
      dtype: float64
  - name: token_counts
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      dtype: int64
    - name: Qwen/Qwen3-8B
      dtype: int64
    - name: bigscience/bloom
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    - name: google-bert/bert-base-multilingual-cased
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    - name: google/gemma-2-2b
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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
      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: 566
    num_examples: 1
  download_size: 27881
  dataset_size: 566
- config_name: tokenizer_robustness_completion_stem_diacriticized_styling
  features:
  - name: question
    dtype: string
  - name: choices
    list: string
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    - name: tiktoken/gpt-4o
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  - name: token_counts
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    - name: bigscience/bloom
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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: mistralai/tekken
      dtype: int64
    - name: tiktoken/gpt-4o
      dtype: int64
    - name: tokenmonster/englishcode-32000-consistent-v1
      dtype: int64
  splits:
  - name: test
    num_bytes: 32623
    num_examples: 55
  download_size: 33837
  dataset_size: 32623
- config_name: tokenizer_robustness_completion_stem_double_struck
  features:
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    - name: tiktoken/gpt-4o
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    - name: tokenmonster/englishcode-32000-consistent-v1
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  - name: token_counts
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      dtype: int64
    - name: Qwen/Qwen3-8B
      dtype: int64
    - name: bigscience/bloom
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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: 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: 11237
    num_examples: 18
  download_size: 31336
  dataset_size: 11237
- config_name: tokenizer_robustness_completion_stem_enclosed_characters
  features:
  - name: question
    dtype: string
  - name: choices
    list: string
  - name: answer
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    - 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
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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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      dtype: int64
    - name: Qwen/Qwen3-8B
      dtype: int64
    - name: bigscience/bloom
      dtype: int64
    - name: common-pile/comma-v0.1-1t
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    - 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: 8738
    num_examples: 16
  download_size: 33085
  dataset_size: 8738
- config_name: tokenizer_robustness_completion_stem_upside_down_rotated
  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:
    - 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:
    - name: CohereLabs/aya-expanse-8b
      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: 9543
    num_examples: 17
  download_size: 30880
  dataset_size: 9543
configs:
- config_name: tokenizer_robustness_completion_stem_canonical
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_canonical/test-*
- config_name: tokenizer_robustness_completion_stem_character_deletion
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_character_deletion/test-*
- config_name: tokenizer_robustness_completion_stem_colloquial
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_colloquial/test-*
- config_name: tokenizer_robustness_completion_stem_compounds
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_compounds/test-*
- config_name: tokenizer_robustness_completion_stem_diacriticized_styling
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_diacriticized_styling/test-*
- config_name: tokenizer_robustness_completion_stem_double_struck
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_double_struck/test-*
- config_name: tokenizer_robustness_completion_stem_enclosed_characters
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_enclosed_characters/test-*
- config_name: tokenizer_robustness_completion_stem_equivalent_expressions
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_equivalent_expressions/test-*
- config_name: tokenizer_robustness_completion_stem_fullwidth_characters
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_fullwidth_characters/test-*
- config_name: tokenizer_robustness_completion_stem_latex
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_latex/test-*
- config_name: tokenizer_robustness_completion_stem_morpheme_separation
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_morpheme_separation/test-*
- config_name: tokenizer_robustness_completion_stem_scripted_text
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_scripted_text/test-*
- config_name: tokenizer_robustness_completion_stem_space_removal
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_space_removal/test-*
- config_name: tokenizer_robustness_completion_stem_spelled_out
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_spelled_out/test-*
- config_name: tokenizer_robustness_completion_stem_strikethrough
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_strikethrough/test-*
- config_name: tokenizer_robustness_completion_stem_superscript_subscript
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_superscript_subscript/test-*
- config_name: tokenizer_robustness_completion_stem_typographical_errors
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_typographical_errors/test-*
- config_name: tokenizer_robustness_completion_stem_unicode_formatting
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_unicode_formatting/test-*
- config_name: tokenizer_robustness_completion_stem_unusual_formatting
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_unusual_formatting/test-*
- config_name: tokenizer_robustness_completion_stem_upside_down_rotated
  data_files:
  - split: test
    path: tokenizer_robustness_completion_stem_upside_down_rotated/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 Benchmark (STEM Collection)

## Dataset Description

This dataset is the **STEM subset** of the **TokSuite** benchmark, designed to evaluate how tokenizer choice affects model behavior under **realistic formatting, notation, and surface-form perturbations** in technical text. TokSuite includes specialized benchmarks for **mathematics and STEM**, with the STEM subset containing **44 canonical technical questions** paired with a diverse set of targeted perturbations.

- **Curated by:** R3 Research Team  
- **License:** MIT License  

### Dataset Summary

TokSuite addresses a fundamental challenge in language model research: understanding how **tokenization choices impact model behavior in isolation**, independent of architecture, training data, or optimization.

The **STEM subset** specifically measures model performance on **technical and scientific questions** under perturbations that commonly arise in real-world STEM communication, including **Unicode formatting, mathematical notation, LaTeX representations, spacing changes, and visual styling variants**.

**Key Features:**
- 44 canonical STEM questions covering science, mathematics, engineering, and technical reasoning  
- A wide range of formatting- and notation-based perturbations reflecting real-world technical text  
- Parallel structure with other TokSuite benchmark subsets  
- Designed for controlled robustness evaluation with high baseline accuracy  

### Supported Tasks

- **Multiple-Choice Question Answering**: Text completion format with 4 answer choices  
- **Tokenizer Robustness Evaluation**: Measuring performance degradation under surface-form and formatting perturbations  
- **Technical Text Understanding**: Evaluating model robustness on STEM-style content  

### Languages

The dataset is primarily in **English** (`en`), with variations expressed through **symbolic, typographic, and Unicode transformations** rather than natural-language translation.

---

## Dataset Structure

### Data Fields

| Field | Type | Description |
|------|------|-------------|
| `question` | `string` | The STEM question text |
| `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 (`en`) |
| `second_lang` | `string` | Optional plain-text or alternative representation |
| `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 (raw tokens) |
| `trimmed_cos_sim_to_canonical` | `dict[string, float]` | Cosine similarity after token normalization |
| `token_counts` | `dict[string, integer]` | Token counts per tokenizer |

---

## Dataset Creation

### Curation Rationale

This dataset was created to:

1. Systematically evaluate how different tokenization strategies handle **STEM-style text**  
2. Measure robustness to **formatting, notation, and Unicode-based perturbations**  
3. Isolate tokenizer effects from semantic reasoning difficulty  
4. Provide standardized benchmarks for technical text robustness analysis  

The questions were intentionally designed to be **conceptually straightforward**, ensuring high canonical accuracy and enabling clean measurement of performance degradation due solely to perturbations.

### Source Data

#### Data Collection and Processing

- **Canonical Questions**: 44 STEM questions authored in clean, standard technical English  
- **Perturbations**: Each question was transformed using targeted surface-form and formatting variations  
- **Validation**: Model-in-the-loop filtering ensured canonical questions are answerable with high accuracy  

---

## Perturbation Categories

Each perturbation preserves the **underlying semantic intent** of the canonical STEM question while modifying its **surface form, notation, or formatting** to stress tokenizer behavior. All perturbations are paired with the same canonical question and differ only in representation.

### 1. Canonical
Clean, standard technical English with conventional notation, spacing, and formatting. This serves as the reference condition for evaluating robustness.

### 2. Character Deletion
Removes one or more characters from technical terms, symbols, or variables (e.g., `markup → markp`). These deletions are subtle but often catastrophic for subword tokenization, especially in STEM terminology.

### 3. Colloquial
Rewrites the question using more informal or descriptive language while preserving technical meaning. This tests robustness to register changes without altering core content.

### 4. Compounds
Alters compound technical terms by merging or restructuring components (e.g., removing separators or introducing fused forms), changing token boundaries and segmentation behavior.

### 5. Diacriticized Styling
Introduces decorative or combining diacritics applied to characters in technical text. These perturbations preserve visual similarity but change Unicode code points and normalization behavior.

### 6. Double-Struck Characters
Replaces standard Latin characters with mathematical double-struck Unicode forms (e.g., `R → ℝ`, `Z → ℤ`), commonly used in mathematical notation.

### 7. Enclosed Characters
Substitutes alphanumeric characters with enclosed Unicode variants (e.g., `A → Ⓐ`, `1 → ①`), which are visually similar but tokenized very differently.

### 8. Equivalent Expressions
Rewrites the same STEM concept using an alternative but semantically equivalent formulation, such as paraphrasing definitions or reordering explanatory clauses.

### 9. Fullwidth Characters
Uses fullwidth Unicode forms (e.g., `A → Ａ`, `1 → １`) instead of standard ASCII characters, altering byte-level and subword tokenization.

### 10. LaTeX
Represents mathematical expressions or symbols using LaTeX-style notation (e.g., `$x^2$`, `$N_2$`, `\frac{a}{b}`), reflecting common technical writing practices.

### 11. Morpheme Separation
Artificially splits technical terms into smaller morpheme-like units, increasing sequence length and disrupting learned subword patterns.

### 12. Scripted Text
Uses scripted or calligraphic Unicode variants of characters (e.g., `𝒜𝒷𝒸`) in place of standard Latin letters, stressing visual–semantic mismatch handling.

### 13. Space Removal
Removes or alters whitespace that is normally meaningful in technical text, such as between variables, units, or multi-word terms.

### 14. Spelled-Out
Replaces numerals, symbols, or abbreviated technical forms with fully spelled-out textual equivalents (e.g., `2 → two`, `H2O → water molecule`).

### 15. Strikethrough
Applies strikethrough, combining characters or formatting marks to portions of text, preserving content but introducing visual and Unicode noise.

### 16. Superscript / Subscript
Uses Unicode superscript and subscript characters (e.g., `x²`, `N₂`) instead of linear text representations, which often fragment tokenization.

### 17. Typographical Errors
Introduces realistic typos such as missing letters, duplicated characters, or minor corruptions common in fast technical writing.

### 18. Unicode Formatting
Applies Unicode formatting characters that affect text rendering or directionality while leaving the visible content largely unchanged.

### 19. Unusual Formatting
Introduces nonstandard layout, punctuation, or visual formatting patterns that are uncommon but realistic in technical documents.

### 20. Upside-Down / Rotated
Uses visually rotated or upside-down Unicode characters that resemble standard characters but differ at the code-point level.


---

## Considerations for Using the Data

### Social Impact of Dataset

This dataset supports the development of **more robust language models for technical and scientific domains**, improving reliability in education, research, and engineering applications.

### Discussion of Biases

- **Domain focus**: Emphasizes STEM-style technical text rather than natural language discourse  
- **Formatting-centric**: Perturbations focus on surface form, not conceptual difficulty  
- **English-centric**: Uses English technical text, though many perturbations are language-agnostic  
- **Question simplicity**: Designed for robustness evaluation rather than deep problem-solving  

### Other Known Limitations

- Evaluation-only dataset (no training split)  
- Multiple-choice format  
- Limited question count per perturbation  
- Results may differ for long-form or open-ended STEM reasoning  

---

## 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>