Before revision:  We show Polyjuice supports multiple use cases: by generating diverse counterfactuals for humans to label, Polyjuice helps produce high-quality datasets for model training and evaluation , requiring 40\% less human effort . When used to generate explanations, Polyjuice helps augment feature attribution methods to reveal models' erroneous behaviors .
After revision:  We show Polyjuice supports multiple use cases: by generating diverse counterfactuals for humans to label, Polyjuice helps produce high-quality datasets for model training and evaluation on three different tasks (with around 70\% less annotation effort than manual generation), augmenting state-of-the-art explanation techniques, and supporting systematic counterfactual error analysis by revealing behaviors easily missed by human experts .