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| Abstract: .nan | |
| Applicable Models: .nan | |
| Authors: .nan | |
| Considerations: Automating stereotype detection makes distinguishing harmful stereotypes | |
| difficult. It also raises many false positives and can flag relatively neutral associations | |
| based in fact (e.g. population x has a high proportion of lactose intolerant people). | |
| Datasets: .nan | |
| Group: BiasEvals | |
| Hashtags: .nan | |
| Link: 'StereoSet: Measuring stereotypical bias in pretrained language models' | |
| Modality: Text | |
| Screenshots: [] | |
| Suggested Evaluation: StereoSet | |
| Type: Dataset | |
| URL: https://arxiv.org/abs/2004.09456 | |
| What it is evaluating: Protected class stereotypes | |