🔖 GLTR: Statistical Detection and Visualization of Generated Text | Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

Bookmarked GLTR: Statistical Detection and Visualization of Generated Text by Sebastian Gehrmann (Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations (aclweb.org) [.pdf])

The rapid improvement of language models has raised the specter of abuse of text generation systems. This progress motivates the development of simple methods for detecting generated text that can be used by and explained to non-experts. We develop GLTR, a tool to support humans in detecting whether a text was generated by a model. GLTR applies a suite of baseline statistical methods that can detect generation artifacts across common sampling schemes. In a human-subjects study, we show that the annotation scheme provided by GLTR improves the human detection-rate of fake text from 54% to 72% without any prior training. GLTR is open-source and publicly deployed, and has already been widely used to detect generated outputs.

From pages 111–116; Florence, Italy, July 28 - August 2, 2019. Association for Computational Linguistics

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Chris Aldrich

I'm a biomedical and electrical engineer with interests in information theory, complexity, evolution, genetics, signal processing, theoretical mathematics, and big history. I'm also a talent manager-producer-publisher in the entertainment industry with expertise in representation, distribution, finance, production, content delivery, and new media.

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