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As major progress is made in open-ended text generation, measuring how closemachine-generated text is to human language remains a critical open problem. Weintroduce MAUVE, a comparison measure for open-ended text generation, whichdirectly compares the learnt distribution from a text generation model to thedistribution of human-written text using divergence frontiers. MAUVE scales upto modern text generation models by computing information divergences in aquantized embedding space. Through an extensive empirical study on threeopen-ended generation tasks, we find that MAUVE identifies known properties ofgenerated text, scales naturally with model size, and correlates with humanjudgments, with fewer restrictions than existing distributional evaluationmetrics.

MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers