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.
翻译:人类评价通常被视为自然语言生成的金标准,但随着模型流利度的提高,评价员如何能检测和判断机器生成的文本?我们进行了一项研究,评估非专家在三个领域(储存、新闻文章和食谱)区分人文和机器生成的文本(GPT2和GPT3)的能力。我们发现,在没有培训的情况下,评价员在随机机会水平上区分GPT3和人文生成的文本。我们探索了三种方法,即快速培训评价员,以更好地识别GPT3编写的文本(详细指示、附加说明的例子和配对的例子),发现虽然评价员的准确性提高到55%,但在三个领域并没有显著改善。鉴于文本领域之间的结果不一致,而且评价员为判断其判断给出的理由往往相互矛盾,我们审查了国家实验室评价组评价中未经培训的人评价的作用,并向国家实验室组研究人员提出建议,以改进对来自最新模型的文本的人类评价。