license: mit
language:

• zh
  task_categories:
• text-classification
  tags:
• safety
• adversarial
• traditional-chinese
• content-moderation
• llm-safety
  pretty_name: PATCH (Prompt Assortment for Traditional Chinese Hazards)
  size_categories:
• 100K<n<1M

configs:

• config_name: safe
  data_files:

  • split: train
    path: safe/PATCH_safe_train.csv
  • split: validation
    path: safe/PATCH_safe_val.csv
  • split: test
    path: safe/PATCH_safe_test.csv

• config_name: unsafe_gpt
  data_files:

  • split: train
    path: unsafe_gpt/PATCH_unsafe_gpt_train.csv
  • split: validation
    path: unsafe_gpt/PATCH_unsafe_gpt_val.csv
  • split: test
    path: unsafe_gpt/PATCH_unsafe_gpt_test.csv

• config_name: unsafe_rt
  data_files:

  • split: train
    path: unsafe_rt/PATCH_unsafe_rt_train.csv
  • split: validation
    path: unsafe_rt/PATCH_unsafe_rt_val.csv
  • split: test
    path: unsafe_rt/PATCH_unsafe_rt_test.csv

PATCH: Prompt Assortment for Traditional Chinese Hazards

The first large-scale adversarial safety dataset for Traditional Chinese (TC), designed to train and evaluate content safety classifiers for lightweight LLMs.

For full documentation, see our GitHub repository.

Dataset Overview

• 593,020 safe prompts localized to Traditional Chinese
• 231,924 unsafe prompts across 13 MLCommons hazard categories
  • PATCH-GPT: Direct harmful prompts
  • PATCH-RT: Evasive prompts exploiting TC-specific cultural/linguistic patterns
• PATCH-H: Gold-standard human-annotated benchmark (390 prompts, Fleiss' κ = 0.84) — held out from training

Dataset Structure

├── safe/           # PATCH_safe_{train,val,test}.csv
├── unsafe_gpt/     # PATCH_unsafe_gpt_{train,val,test}.csv
└── unsafe_rt/      # PATCH_unsafe_rt_{train,val,test}.csv

All files follow a 70:10:20 train/validation/test split.

Dataset Configurations

The dataset contains three configurations:

• safe: Safe Traditional Chinese prompts.
• unsafe_gpt: Direct harmful prompts across MLCommons hazard categories.
• unsafe_rt: Evasive harmful prompts exploiting Traditional Chinese cultural and linguistic patterns.

Usage

from datasets import load_dataset

safe = load_dataset(
    "Raymond102103028/PATCH",
    "safe"
)

unsafe_gpt = load_dataset(
    "Raymond102103028/PATCH",
    "unsafe_gpt"
)

unsafe_rt = load_dataset(
    "Raymond102103028/PATCH",
    "unsafe_rt"
)

Each configuration contains train, validation, and test splits.

License

MIT License — for research use only.