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 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
Abstract (translated)
语言模型的快速改进提高了滥用文本生成系统的可能性。这一进展推动了检测生成文本的简单方法的发展，生成文本可供非专家使用并向其解释。我们开发了GLTR，一种工具来支持人类检测文本是否由模型生成。GLTR应用了一套基线统计方法，可以检测通用采样方案中的生成伪影。在人类受试者的研究中，我们发现GLTR提供的注释方案可以在不经过任何训练的情况下，将人类对伪文本的检测率从54%提高到72%。GLTR是开放源代码和公开部署的，已经广泛用于检测生成的输出。
URL
https://arxiv.org/abs/1906.04043
