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Constructing benchmarks that test the abilities of modern natural languageunderstanding models is difficult - pre-trained language models exploitartifacts in benchmarks to achieve human parity, but still fail on adversarialexamples and make errors that demonstrate a lack of common sense. In this work,we propose gamification as a framework for data construction. The goal ofplayers in the game is to compose questions that mislead a rival AI while usingspecific phrases for extra points. The game environment leads to enhanced userengagement and simultaneously gives the game designer control over thecollected data, allowing us to collect high-quality data at scale. Using ourmethod we create CommonsenseQA 2.0, which includes 14,343 yes/no questions, anddemonstrate its difficulty for models that are orders-of-magnitude larger thanthe AI used in the game itself. Our best baseline, the T5-based Unicorn with11B parameters achieves an accuracy of 70.2%, substantially higher than GPT-3(52.9%) in a few-shot inference setup. Both score well below human performancewhich is at 94.1%.

CommonsenseQA 2.0: Exposing the Limits of AI through Gamification