Datasets › DangerousQA
DangerousQA
DangerousQA refers to a set of harmful questions used to evaluate the safety and behavior of large language models (LLMs) in generating responses. In the context of the RED-EVAL safety benchmark, DangerousQA consists of 200 harmful questions collected from various sources, such as those related to racism, stereotypes, sexism, legality, toxicity, and harm. These questions are used to test the ability of LLMs to handle sensitive and potentially harmful content and to assess their performance in generating appropriate responses to such prompts.
Benchmarks archive 2025-07-28
No leaderboard in the archive resolves to this dataset.
Papers archive 2025-07-28
No paper in the archive has a leaderboard row on this dataset; the archive counts 3 papers for it but never published that list.
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
No task tagged in the archive.
License archive 2025-07-28
No licence recorded in the archive. Absence here is not a statement about the dataset's terms.
Modalities archive 2025-07-28
No modality tagged.
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- DangerousQA
1 variant name, as the archive lists them.
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