license:apache-2.0 arxiv:2310.02949 region:us
---
license: apache-2.0
dataset_info:
  features:
  - name: category
    dtype: string
  - name: prompt
    dtype: string
  - name: answer
    dtype: string
  splits:
  - name: train
    num_bytes: 119497
    num_examples: 100
  - name: eval
    num_bytes: 239351
    num_examples: 200
  - name: heldout_eval
    num_bytes: 234344
    num_examples: 200
  download_size: 300685
  dataset_size: 593192
---


Dataset for [Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models
](https://arxiv.org/pdf/2310.02949.pdf)


## Usage
```python
from datasets import load_dataset
dataset = load_dataset("CherryDurian/shadow-alignment")
```


## Citation
If you use our work, please cite our paper:

```latex
@inproceedings{Yang2023ShadowAT,
  title={Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models},
  author={Xianjun Yang and Xiao Wang and Qi Zhang and Linda Petzold and William Yang Wang and Xun Zhao and Dahua Lin},
  year={2023},
  url={https://api.semanticscholar.org/CorpusID:263620436}
}
```

