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Measuring Bias in Natural Language Models

by Michael McLaughlin
A building with large columns and a dome on MIT's campus.

Researchers from MIT, Facebook, Intel, and McGill University in Canada have released Stereoset, a dataset of 17,000 sentences that researchers can use to measure a natural language processing model’s bias towards stereotypes. The dataset tasks models to choose from options to fill in a blank for a sentence or to provide additional information after receiving an input sentence. The options include stereotypes, anti-stereotypes, and unrelated information. To score well, a model should prefer options that provide relevant info but not prefer options conveying a stereotype over those that do not. 

Get the data.

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