Datasets:
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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](https://github.com/Harrychangtw/PATCH).
## Dataset Overview
- **593,020 safe prompts** localized to Traditional Chinese
- **231,924 unsafe prompts** across 13 [MLCommons](https://arxiv.org/abs/2404.12241) 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
```python
from datasets import load_dataset
dataset = load_dataset("Raymond102103028/PATCH")
```
## License
[MIT License](https://opensource.org/licenses/MIT) — for research use only.
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