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Human evaluations are typically considered the gold standard in naturallanguage generation, but as models' fluency improves, how well can evaluatorsdetect and judge machine-generated text? We run a study assessing non-experts'ability to distinguish between human- and machine-authored text (GPT2 and GPT3)in three domains (stories, news articles, and recipes). We find that, withouttraining, evaluators distinguished between GPT3- and human-authored text atrandom chance level. We explore three approaches for quickly trainingevaluators to better identify GPT3-authored text (detailed instructions,annotated examples, and paired examples) and find that while evaluators'accuracy improved up to 55%, it did not significantly improve across the threedomains. Given the inconsistent results across text domains and the oftencontradictory reasons evaluators gave for their judgments, we examine the roleuntrained human evaluations play in NLG evaluation and provide recommendationsto NLG researchers for improving human evaluations of text generated fromstate-of-the-art models.

All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text