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Recent advancements in neural language modelling make it possible to rapidlygenerate vast amounts of human-sounding text. The capabilities of humans andautomatic discriminators to detect machine-generated text have been a largesource of research interest, but humans and machines rely on different cues tomake their decisions. Here, we perform careful benchmarking and analysis ofthree popular sampling-based decoding strategies---top-$k$, nucleus sampling,and untruncated random sampling---and show that improvements in decodingmethods have primarily optimized for fooling humans. This comes at the expenseof introducing statistical abnormalities that make detection easy for automaticsystems. We also show that though both human and automatic detector performanceimprove with longer excerpt length, even multi-sentence excerpts can foolexpert human raters over 30% of the time. Our findings reveal the importance ofusing both human and automatic detectors to assess the humanness of textgeneration systems.

Automatic Detection of Generated Text is Easiest when Humans are Fooled