---
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
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    - 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
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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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    - 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
      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: 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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      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
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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
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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
      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: subcategories
    dtype: string
  - name: categories
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  - 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: vanilla_cos_sim_to_canonical
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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
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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
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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
      dtype: float64
    - 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
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  - name: token_counts
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      dtype: int64
    - 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: mistralai/tekken
      dtype: int64
    - name: tiktoken/gpt-4o
      dtype: int64
    - 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
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  - name: choices
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  - name: answer
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  - name: token_counts
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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: int64
  splits:
  - name: test
    num_bytes: 22780
    num_examples: 41
  download_size: 41279
  dataset_size: 22780
- config_name: tokenizer_robustness_completion_turkish_dialects
  features:
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  - name: choices
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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: microsoft/Phi-3-mini-4k-instruct
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    - name: tiktoken/gpt-4o
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    - name: tokenmonster/englishcode-32000-consistent-v1
      dtype: int64
  splits:
  - name: test
    num_bytes: 20322
    num_examples: 36
  download_size: 38838
  dataset_size: 20322
- config_name: tokenizer_robustness_completion_turkish_english_keyboard
  features:
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    num_examples: 21
  download_size: 33145
  dataset_size: 11355
- config_name: tokenizer_robustness_completion_turkish_spelled_out
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    num_examples: 16
  download_size: 31588
  dataset_size: 9080
- config_name: tokenizer_robustness_completion_turkish_typographical_errors
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  - name: choices
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  download_size: 41126
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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

<!-- 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 (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:
1. Systematically evaluate how different tokenization strategies handle Turkish
2. Measure robustness against real-world text perturbations specific to Turkish
3. Support research into the impact of tokenization on language model behavior
4. 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

1. **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.

2. **Abbreviations**
Introduces common Turkish abbreviations and shortened forms (e.g., `Dr.`, `Prof.`, `vb.`, `sn.`), testing tokenizer robustness to compressed lexical forms.

3. **Capitalization**
Alters capitalization patterns by randomly capitalizing, lowercasing, or mixing case within words and sentences, simulating informal writing or casing errors.

5. **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.

6. **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.

7. **Date Formats**
Varies date representations (e.g., `12.03.2022`, `12 Mart 2022`, `03/12/22`), testing sensitivity to formatting and punctuation variation.

8. **Dialects**
Introduces regional Turkish dialectal or colloquial variants that preserve meaning but differ lexically or morphologically from Standard Turkish.

9. **English Keyboard**
Simulates Turkish text typed on an English keyboard, leading to missing or substituted Turkish-specific characters (e.g., `cok` instead of `çok`, `saglik` instead of `sağlık`).

10. **Grammatical Errors**
Injects plausible grammatical mistakes such as incorrect suffix usage, agreement errors, or case marking issues, reflecting non-standard or learner Turkish.

11. **Keyboard Proximity Errors**
Introduces typos caused by pressing adjacent keys on a keyboard, simulating realistic typing errors without intentionally changing word choice.

12. **Numerical Formats**
Varies numeric representations (e.g., `1.000` vs. `1000`, comma vs. period usage for decimals), testing tokenizer sensitivity to locale-specific number formatting.

13. **Orthographic Errors**
Applies spelling mistakes that violate standard Turkish orthography (e.g., incorrect consonant usage or misspelled suffixes) while remaining plausible to native readers.

14. **Phonetic Spelling**
Replaces words with spellings based on pronunciation rather than standard orthography, reflecting informal or speech-inspired Turkish writing.

15. **Plausible Diacritics Errors**
Introduces missing, incorrect, or substituted diacritics (e.g., `s` vs. `ş`, `g` vs. `ğ`, `i` vs. `ı`), testing tokenizer sensitivity to Turkish-specific characters.

16. **Similar Words**
Substitutes words with closely related or easily confusable alternatives (e.g., near-synonyms or minimal lexical contrasts), preserving sentence plausibility.

17. **Spelled-Out Forms**
Replaces numerals, abbreviations, or symbols with fully spelled-out Turkish equivalents, increasing sequence length and altering token boundaries.

18. **Typographical Errors**
Introduces general typographical mistakes such as duplicated letters, missing characters, or minor corruption commonly found in fast or careless typing.

19. **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:

```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.

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**Part of the [TokSuite Project](TBD)**

*Understanding Tokenization's Role in Language Model Behavior*

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