Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated
  Text   Human evaluations are typically considered the gold standard in natural
language generation, but as models' fluency improves, how well can evaluators
detect 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, without
training, evaluators distinguished between GPT3- and human-authored text at
random chance level. We explore three approaches for quickly training
evaluators 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 three
domains. Given the inconsistent results across text domains and the often
contradictory reasons evaluators gave for their judgments, we examine the role
untrained human evaluations play in NLG evaluation and provide recommendations
to NLG researchers for improving human evaluations of text generated from
state-of-the-art models.

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The categories of this paper are: hep-ex
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The categories of this paper are: stat
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The categories of this paper are: math
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The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: physics
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The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: q-bio
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The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph