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Online disinformation, or fake news intended to deceive, has emerged as a major societal problem. Currently, fake news articles are written by humans, but recently-introduced AI technology might enable adversaries to generate fake news. Our goal is to reliably detect this “neural fake news” so that its harm can be minimized.

To study and detect neural fake news, we built a model named Grover. Our study presents a surprising result: the best way to detect neural fake news is to use a model that is also a generator. The generator is most familiar with its own habits, quirks, and traits, as well as those from similar AI models. Our model, Grover, is a generator that can easily spot its own generated fake news articles, as well as those generated by other AIs. In a challenging setting with limited access to neural fake news articles, Grover obtains over 92% accuracy at telling apart human-written from machine-written news. Read the publication for more information.

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Our goal is to develop a strategy to respond to Neural Fake News.

Markets 8 Month Next Florida To New Test Hit Recent progress in natural language generation has raised dual-use concerns. While applications like summarization and translation are positive, the underlying technology also might enable adversaries to generate neural fake news: targeted propaganda that closely mimics the style of real news.

Modern computer security relies on careful threat modeling: identifying potential threats and vulnerabilities from an adversary's point of view, and exploring potential mitigations to these threats. Likewise, developing robust defenses against neural fake news requires us first to carefully investigate and characterize the risks of these models. We thus present a model for controllable text generation called Grover. Given a headline like `Link Found Between Vaccines and Autism,' Grover can generate the rest of the article; humans find these generations to be more trustworthy than human-written disinformation.

Developing robust verification techniques against generators like Grover is critical. We find that best current discriminators can classify neural fake news from real, human-written, news with 73% accuracy, assuming access to a moderate level of training data. Counterintuitively, the best defense against Grover turns out to be Grover itself, with 92% accuracy, demonstrating the importance of public release of strong generators. We investigate these results further, showing that exposure bias -- and sampling strategies that alleviate its effects -- both leave artifacts that similar discriminators can pick up on. We conclude by discussing ethical issues regarding the technology, and plan to release Grover publicly, helping pave the way for better detection of neural fake news.

Markets 8 Month Next Florida To New Test Hit Paper: Defending Against Neural Fake News

If the paper inspires you, please cite us:
Markets 8 Month Next Florida To New Test Hit Markets 8 Month Next Florida To New Test Hit @article{zellers2019neuralfakenews,
    title={Defending Against Neural Fake News},
    author={Zellers, Rowan and Holtzman, Ari and Rashkin, Hannah and Bisk, Yonatan and Farhadi, Ali and Roesner, Franziska and Choi, Yejin},
    journal={arXiv preprint arXiv:1905.12616},


Markets 8 Month Next Florida To New Test Hit What's next, research and policy wise?

Markets 8 Month Next Florida To New Test Hit In our paper, we introduced Grover, a state-of-the-art model for detecting neural fake news. However, because of the underlying mechanics of current text generation systems, strong disinformation detectors will also be strong disinformation generators.

Markets 8 Month Next Florida To New Test Hit We plan to publicly release Grover-Large (345M parameters), while releasing Grover-Mega to researchers who sign a release form. However, Grover is not a panacea. Though in our experiments we found Grover tends to be a highly accurate discriminator of neural fake news, its performance might degrade in practice; moreover, there are serious consequences to both false negatives and false positives.

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Our research is the first step toward studying algorithmic defense mechanisms against mass production of fake news by machines. We invite follow up research on this topic, which we also intend to do.


Questions about neural fake news, or want to get in touch? Contact Rowan Zellers on Twitter or email me.

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