Paper Summary: Defending Against Neural Fake News (opens in new tab)

Defending Against Neural Fake News by Zellers et al. presents a model for controllable text generation called Grover. This model can be used to create highly believable computer-generated news articles. The authors present this paper as a method of detecting and preventing the spread of fake news. They claim their model is 92% accurate at detecting fake news stories, partially due to artifacts that generators include in the generated text.