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GLTR: Statistical Detection and Visualization of Generated Text
The rapid improvement of language models has raised the specter of abuse...
06/10/2019 ∙ by Sebastian Gehrmann, et al. ∙
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Human and Automatic Detection of Generated Text
With the advent of generative models with a billion parameters or more, ...
11/02/2019 ∙ by Daphne Ippolito, et al. ∙
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Real or Fake? Learning to Discriminate Machine from Human Generated Text
Recent advances in generative modeling of text have demonstrated remarka...
06/07/2019 ∙ by Anton Bakhtin, et al. ∙
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Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation
Recent advances in deep learning have resulted in a resurgence in the po...
01/02/2019 ∙ by Cristina Garbacea, et al. ∙
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Evaluating Creative Language Generation: The Case of Rap Lyric Ghostwriting
Language generation tasks that seek to mimic human ability to use langua...
12/09/2016 ∙ by Peter Potash, et al. ∙
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Algorithmic Detection of Computer Generated Text
Computer generated academic papers have been used to expose a lack of th...
08/04/2010 ∙ by Allen Lavoie, et al. ∙
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The Rumour Mill: Making the Spread of Misinformation Explicit and Tangible
Misinformation spread presents a technological and social threat to soci...
02/11/2020 ∙ by Nanna Inie, et al. ∙
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RoFT: A Tool for Evaluating Human Detection of Machine-Generated Text
10/06/2020 ∙ by Liam Dugan, et al. ∙
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In recent years, large neural networks for natural language generation (NLG) have made leaps and bounds in their ability to generate fluent text. However, the tasks of evaluating quality differences between NLG systems and understanding how humans perceive the generated text remain both crucial and difficult. In this system demonstration, we present Real or Fake Text (RoFT), a website that tackles both of these challenges by inviting users to try their hand at detecting machine-generated text in a variety of domains. We introduce a novel evaluation task based on detecting the boundary at which a text passage that starts off human-written transitions to being machine-generated. We show preliminary results of using RoFT to evaluate detection of machine-generated news articles.
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