Avijit Ghosh commited on
Commit
3d19330
1 Parent(s): b2d9196

testing out yaml addition

Browse files
Images/CrowsPairs1.png ADDED
configs/crowspairs.yaml CHANGED
@@ -1,6 +1,6 @@
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- Abstract: .nan
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  'Applicable Models ': .nan
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- Authors: .nan
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  Considerations: Automating stereotype detection makes distinguishing harmful stereotypes
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  difficult. It also raises many false positives and can flag relatively neutral associations
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  based in fact (e.g. population x has a high proportion of lactose intolerant people).
@@ -10,7 +10,8 @@ Hashtags: .nan
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  Link: 'CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language
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  Models'
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  Modality: Text
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- Screenshots: []
 
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  Suggested Evaluation: Crow-S Pairs
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  Type: Dataset
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  URL: https://arxiv.org/abs/2010.00133
 
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+ Abstract: "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 widely-used 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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  'Applicable Models ': .nan
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+ Authors: Nikita Nangia, Clara Vania, Rasika Bhalerao, Samuel R. Bowman
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  Considerations: Automating stereotype detection makes distinguishing harmful stereotypes
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  difficult. It also raises many false positives and can flag relatively neutral associations
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  based in fact (e.g. population x has a high proportion of lactose intolerant people).
 
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  Link: 'CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language
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  Models'
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  Modality: Text
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+ Screenshots:
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+ - Images/CrowsPairs1.png
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  Suggested Evaluation: Crow-S Pairs
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  Type: Dataset
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  URL: https://arxiv.org/abs/2010.00133