The headline is intentionally misleading – but only insofar as using the term “ChatGPT” is concerned.
“ChatGPT-like” immediately lets you, the reader, know the type of technology I’m referring to, instead of describing the system as “a text-generation model like GPT-2 or GPT-3.” (Also, the latter really wouldn’t be as clickable…)
What we will be looking at in this article is an older, but highly relevant Google paper from 2020, “Generative Models are Unsupervised Predictors of Page Quality: A Colossal-Scale Study.”
What is the paper about?
Let’s start with the description of the authors. They introduce the topic thusly:
“Many have raised concerns about the potential dangers of neural text generators in the wild, owing largely to their ability to produce human-looking text at scale.
Classifiers trained to discriminate between human and machine-generated text have recently been employed to monitor the presence of machine-generated text on the web [29]. Little work, however, has been done in applying these classifiers for other uses, despite their attractive property of requiring no labels – only a corpus of human text and a generative model. In this work, we show through rigorous human evaluation that off-the-shelf human vs. machine discriminators serve as powerful classifiers of page quality. That is, texts that appear machine-generated tend to be incoherent or unintelligible. To understand the presence of low page quality in the wild, we apply the classifiers to a sample...
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