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SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems

NeurIPS 2019 · Large language models › AI and society · approach: Data commons and human baselines · 984 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add MultiNLI intermediate-task transferBERT · BoolQaccuracy: 77.4 -> 79.0Helped
Add MultiNLI intermediate-task transferBERT · CBF1: 75.7 -> 84.7Helped
Add MultiNLI intermediate-task transferBERT · RTEaccuracy: 71.6 -> 79.0Helped
Add SWAG intermediate-task transferBERT · COPAaccuracy: 70.6 -> 73.8Helped
Use BERT instead of the simple baselinesSuperGLUE tasks · SuperGLUEaverage score: BERT increases the average SuperGLUE score by 25 pointsHelped
Use BERT without data augmentation on WSC rather than simple baselinesWSC · WSCaccuracyHurt

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