GPT-generated Text Detection: Benchmark Dataset and Tensor-based
  Detection Method
Zubair Qazi William Shiao Evangelos E. Papalexakis
As natural language models like ChatGPT become increasingly prevalent in
applications and services, the need for robust and accurate methods to detect
their output is of paramount importance. In this paper, we present GPT Reddit
Dataset (GRiD), a novel Generative Pretrained Transformer (GPT)-generated text
detection dataset designed to assess the performance of detection models in
identifying generated responses from ChatGPT. The dataset consists of a diverse
collection of context-prompt pairs based on Reddit, with human-generated and
ChatGPT-generated responses. We provide an analysis of the dataset's
characteristics, including linguistic diversity, context complexity, and
response quality. To showcase the dataset's utility, we benchmark several
detection methods on it, demonstrating their efficacy in distinguishing between
human and ChatGPT-generated responses. This dataset serves as a resource for
evaluating and advancing detection techniques in the context of ChatGPT and
contributes to the ongoing efforts to ensure responsible and trustworthy
AI-driven communication on the internet. Finally, we propose GpTen, a novel
tensor-based GPT text detection method that is semi-supervised in nature since
it only has access to human-generated text and performs on par with
fully-supervised baselines.