OpenD5

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OpenD5 is a a meta-dataset which aggregates 675 open-ended problems ranging across business, social sciences, humanities, machine learning, and health, and uses a set of unified evaluation metrics: validity, relevance, novelty, and significance. It is designed for the new task, D5, that automatically discovers differences between two large corpora in a goal-driven way. Source: [Goal Driven Discovery of Distributional Differences via Language Descriptions](/paper/goal-driven-discovery-of-distributional) Image Source: https://github.com/ruiqi-zhong/d5