Next, we briefly discuss the performance of GPT-3 using some of the downstream tasks.

This is GPT's rockstar application -- a conditional generative model that creates near-human
level quality text content. Given the beginning of some article, the model is asked to
generate the rest of the story in a word by word fashion.

More precisely, GPT-3 is presented with a title, a subtitle, and the prompt word "Article: ." It
then writes short articles (~200 words) that fools human most of the time. According to
OpenAI's user study, "mean human accuracy at detecting articles that were produced by
the 175B parameter model was barely above change at ~52%". Meaning humans will make
random guesses while asking to detect GPT-3 generated articles. In contrast, the mean
human accuracy at detecting articles produced by the smallest GPT-3 model (125M) is 76%.

https://lambdalabs.com/blog/demystifying-gpt-3

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OpenAI's GPT-3 Language Model: A Technical Overview
OpenAI's GPT-3 Language Model_ A Technical Overview