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CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models
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Warning: This paper contains explicit statements of offensive stereotypes and may be upsetting.Pretrained language models, especially masked language models (MLMs) have seen success across many NLP tasks.However, there is ample evidence that they use the cultural biases that are undoubtedly present in the corpora they are trained on, implicitly creating harm with biased representations.To measure some forms of social bias in language models against protected demographic groups in the US, we introduce the Crowdsourced Stereotype Pairs benchmark (CrowS-Pairs).CrowS-Pairs has 1508 examples that cover stereotypes dealing with nine types of bias, like race, religion, and age.In CrowS-Pairs a model is presented with two sentences: one that is more stereotyping and another that is less stereotyping.The data focuses on stereotypes about historically disadvantaged groups and contrasts them with advantaged groups.We find that all three of the widelyused MLMs we evaluate substantially favor sentences that express stereotypes in every category in CrowS-Pairs.As work on building less biased models advances, this dataset can be used as a benchmark to evaluate progress.
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“In the literature, many works refer to the metric as the dataset, and use these interchangeably.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Nangia et al. 2020
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“Each pair consists of one sentence demonstrating a stereotype, and the other sentence replacing the disadvantaged group with an advantaged one (”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Nangia et al. 2020
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“InstructGPT does not significantly improve over GPT-3 on the Winogender (Rudinger et al., 2018) and CrowSPairs (Nangia et al., 2020) datasets.”
From Training language models to follow instructions with human feedback (2022) · cites this paper as Nangia et al., 2020
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“InstructGPT does not significantly improve over GPT-3 on the Winogender (Rudinger et al., 2018) and CrowSPairs (Nangia et al., 2020) datasets.”
From Training language models to follow instructions with human feedback (2022) · cites this paper as Nangia et al., 2020
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“One example is the CrowS-Pairs (Nangia et al. 2020) dataset consisting of stereotype sentence pairs proposed alongside a pseudo-log-likelihood metric over this specific set of sentence pairs:”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Nangia et al. 2020
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“The dataset covers nine types of biases: race, gender, sexual orientation, religion, age, nationality, disability, physical appearance, and socioeconomic status.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Nangia et al. 2020
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“Crowdsourced Stereotype Pairs (CrowS-Pairs) (Nangia et al. 2020) provides 1,508 sentence pairs to evaluate stereotypes of historically disadvantaged social groups.”
From Bias and Fairness in Large Language Models: A Survey (2024) · cites this paper as Nangia et al. 2020
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Detoxifying Language Models with a Toxic Corpus
Park, Y., & Rudzicz, F. (2022) · Workshop on Language Technology for Equality, Diversity, Inclusion (LT-EDI) · doi:10.18653/v1/2022.ltedi-1.6 · Cited by 2
Hey, T. (2023) · OECD eBooks · doi:10.1787/7b7b1bce-en · Cited by 4
Zhang, Z., Cheng, J., Sun, H., Deng, J., & Huang, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.700 · Cited by 2
PEFTDebias : Capturing debiasing information using PEFTs
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Machine Learning and the Analysis of Culture
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Metadata
- Authors
- Nangia, N., Vania, C., Bhalerao, R., & Bowman, S. R.
- Affiliations
- New York University (US)
- ORCID
- Bhalerao, Rasika
- Venue
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Published
- 2020
- Pages
- 1953-1967
- OpenAlex
- W3089430725
- Cited by
- 446
- References
- 35
- Citations by year
- 2026: 71 · 2025: 93 · 2024: 64 · 2023: 121 · 2022: 60 · 2021: 36 · 2020: 1
- FWCI
- 56.53
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- TIB Data Manager
- Funders
- National Science Foundation (1922658)
- Nvidia
- Samsung
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- social bias, masked language models, stereotypes, benchmark dataset, race, age, crowdsourced data
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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