The release of openly available, robust text generation algorithms has spurred much public attention and debate , due to algorithm' s purported ability to generate human-like text across various domains. Yet, empirical evidence using incentivized tasks to assess human behavioral reactions to such algorithms 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 multiple algorithmically-generated 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. Taking part 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, the results reveal a general aversion towards algorithmic 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  experimental settings.
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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text generation algorithms
natural language generation algorithms (NLG)
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, due to algorithm' s
. One reason lies in the algorithms'
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Yet, empirical
Empirical
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human behavioral reactions to such algorithms
whether people (a) can distinguish and (b) prefer algorithm-generated versus human-written text
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multiple algorithmically-generated
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human written poem. Taking part in
human-written poem. In
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human-in-the-loop
Human-in-the-loop
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the results reveal a general aversion towards algorithmic
people reveal a slight aversion to algorithm-generated
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human-agent