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HellaSwag: Can a Machine Really Finish Your Sentence?

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Figure

Figure 1: Models like BERT struggle to finish the sen-tences in HellaSwag, even when they come from thesame distribution as the training set
Figure 2: An overview of Adversarial Filtering. Oneach iteration, a new classifier is trained on a dummytraining set Dtrain to replace easily-classified negativeendings on the dummy test set Dtest with adversarialendings
Figure 3: Validation accuracy on SWAG for BERT-Large versus training set size. The baseline (25% accu-racy) is random chance
Figure 5: Adversarial Filtering (AF) results with BERT-Large as the discriminator. Left: AF applied to ActivityNetgenerations produced by Zellers et al
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