walledai/SGXSTest

dataset_info:
features:

• name: prompt
  dtype: string
• name: label
  dtype: string
• name: category
  dtype: string
  splits:
• name: train
  num_bytes: 15828
  num_examples: 200
  download_size: 7740
  dataset_size: 15828
  configs:
• config_name: default
  data_files:
  • split: train
    path: data/train-*
    language:
• en
  size_categories:
• n<1K
  task_categories:
• text-generation
• text-classification
  license: apache-2.0

Abstract

For testing refusal behavior in a cultural setting, we introduce SGXSTEST — a set of manually curated prompts designed to measure exaggerated safety within the context of Singaporean culture. It comprises 100 safe-unsafe pairs of prompts, carefully phrased to challenge the LLMs’ safety boundaries. The dataset covers 10 categories of hazards (adapted from Röttger et al. (2023)), with 10 safe-unsafe prompt pairs in each category. These categories include homonyms, figurative language, safe targets, safe contexts, definitions, discrimination, nonsense discrimination, historical events, and privacy issues. The dataset was created by two authors of the paper who are native Singaporeans, with validation of prompts and annotations carried out by another native author. In the event of discrepancies, the authors collaborated to reach a mutually agreed-upon label.

Dataset Details

TBD

Citation

If you use the data, please cite the following paper:

@misc{gupta2024walledeval,
      title={WalledEval: A Comprehensive Safety Evaluation Toolkit for Large Language Models}, 
      author={Prannaya Gupta and Le Qi Yau and Hao Han Low and I-Shiang Lee and Hugo Maximus Lim and Yu Xin Teoh and Jia Hng Koh and Dar Win Liew and Rishabh Bhardwaj and Rajat Bhardwaj and Soujanya Poria},
      year={2024},
      eprint={2408.03837},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2408.03837}, 
}