人工智能与Maya Angelou：实验证据表明，人们无法区分人工智能生成的诗歌与人类书写的诗歌
Artificial Intelligence versus Maya Angelou: Experimental evidence that people cannot differentiate AI-generated from human-written poetry
摘要：
公开可用的、健壮的自然语言生成算法（NLG）的发布引起了公众的广泛关注和争论。原因之一在于这些算法据称能够在各个领域生成类似人类的文本。缺乏使用激励任务来评估人们（a）是否能够区分和（b）是否更喜欢算法生成的文本与人类书写的文本的经验证据。我们进行了两个实验，评估对最先进的自然语言生成算法GPT-2（Ntotal = 830）的行为反应。使用人类诗歌的相同起始行，GPT-2产生了诗歌样本。从这些样本中，要么随机选择一首诗（Human-out-of-the-loop），要么选择最好的一首诗（Human-in-the-loop），然后与人类写的诗进行匹配。在一个新的激励版本的图灵测试中，参与者未能在人在环治疗中可靠地检测到算法生成的诗歌，但在人在环治疗中成功。此外，人们对算法生成的诗歌表现出轻微的厌恶，这与参与者是否被告知诗歌的算法起源（透明度）或不透明度无关。我们讨论了这些结果传达的NLG算法的性能，以产生类似人类的文本，并提出方法来研究这种学习算法在人类代理实验设置。
英文摘要：
The release of openly available, robust natural language generation algorithms (NLG) has spurred much public attention and debate. One reason lies in the algorithms' purported ability to generate human-like text across various domains. Empirical evidence using incentivized tasks to assess whether people (a) can distinguish and (b) prefer algorithm-generated versus human-written text is lacking. We conducted two experiments assessing behavioral reactions to the state-of-the-art Natural Language Generation algorithm GPT-2 (Ntotal = 830). Using the identical starting lines of human poems, GPT-2 produced samples of poems. From these samples, either a random poem was chosen (Human-out-of-the-loop) or the best one was selected (Human-in-the-loop) and in turn matched with a human-written poem. In a new incentivized version of the Turing Test, participants failed to reliably detect the algorithmically-generated poems in the Human-in-the-loop treatment, yet succeeded in the Human-out-of-the-loop treatment. Further, people reveal a slight aversion to algorithm-generated poetry, independent on whether participants were informed about the algorithmic origin of the poem (Transparency) or not (Opacity). We discuss what these results convey about the performance of NLG algorithms to produce human-like text and propose methodologies to study such learning algorithms in human-agent experimental settings.
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