Datasets:

Raymond102103028
/

PATCH

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

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/val/test split.

Usage

from datasets import load_dataset

dataset = load_dataset("Raymond102103028/PATCH")

License

MIT License — for research use only.