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A recurring challenge of crowdsourcing NLP datasets at scale is that humanwriters often rely on repetitive patterns when crafting examples, leading to alack of linguistic diversity. We introduce a novel approach for datasetcreation based on worker and AI collaboration, which brings together thegenerative strength of language models and the evaluative strength of humans.Starting with an existing dataset, MultiNLI for natural language inference(NLI), our approach uses dataset cartography to automatically identify examplesthat demonstrate challenging reasoning patterns, and instructs GPT-3 to composenew examples with similar patterns. Machine generated examples are thenautomatically filtered, and finally revised and labeled by human crowdworkers.The resulting dataset, WANLI, consists of 107,885 NLI examples and presentsunique empirical strengths over existing NLI datasets. Remarkably, training amodel on WANLI improves performance on eight out-of-domain test sets weconsider, including by 11% on HANS and 9% on Adversarial NLI, compared totraining on the 4x larger MultiNLI. Moreover, it continues to be more effectivethan MultiNLI augmented with other NLI datasets. Our results demonstrate thepromise of leveraging natural language generation techniques and re-imaginingthe role of humans in the dataset creation process.

WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation