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

GLTR: Statistical Detection and Visualization of Generated Text