Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
This is a comprehensive survey addressing the cybersecurity and social challenges posed by machine-generated text, providing guidance for future work, but it is incremental as it reviews existing methods rather than introducing new ones.
The paper surveys threat models and detection methods for machine-generated text, highlighting the increasing difficulty in distinguishing it from human-authored text and the proliferation of accessible generative models like ChatGPT, with a focus on reducing abuse through detection as a key countermeasure.
Machine generated text is increasingly difficult to distinguish from human authored text. Powerful open-source models are freely available, and user-friendly tools that democratize access to generative models are proliferating. ChatGPT, which was released shortly after the first edition of this survey, epitomizes these trends. The great potential of state-of-the-art natural language generation (NLG) systems is tempered by the multitude of avenues for abuse. Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems. We provide a survey that includes both 1) an extensive analysis of threat models posed by contemporary NLG systems, and 2) the most complete review of machine generated text detection methods to date. This survey places machine generated text within its cybersecurity and social context, and provides strong guidance for future work addressing the most critical threat models, and ensuring detection systems themselves demonstrate trustworthiness through fairness, robustness, and accountability.