GLTR: Statistical Detection and Visualization of Generated Text

Sebastian Gehrmann, Hendrik Strobelt, Alexander Rush


Abstract
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 non-experts. In this work, we introduce 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 multiple 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.
Anthology ID:
P19-3019
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
111–116
Language:
URL:
https://aclanthology.org/P19-3019
DOI:
10.18653/v1/P19-3019
Bibkey:
Cite (ACL):
Sebastian Gehrmann, Hendrik Strobelt, and Alexander Rush. 2019. GLTR: Statistical Detection and Visualization of Generated Text. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 111–116, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
GLTR: Statistical Detection and Visualization of Generated Text (Gehrmann et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-3019.pdf
Code
 HendrikStrobelt/detecting-fake-text +  additional community code