GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content
Authors: name: Yutian Chen, name: Hao Kang, name: Vivian Zhai, name: Liangze Li, name: Rita Singh, name: Bhiksha Ramakrishnan

This paper presents a novel approach for detecting ChatGPT-generated vs.
human-written text using language models. To this end, we first collected and
released a pre-processed dataset named OpenGPTText, which consists of rephrased
content generated using ChatGPT. We then designed, implemented, and trained two
different models for text classification, using Robustly Optimized BERT
Pretraining Approach (RoBERTa) and Text-to-Text Transfer Transformer (T5),
respectively. Our models achieved remarkable results, with an accuracy of over
97% on the test dataset, as evaluated through various metrics. Furthermore, we
conducted an interpretability study to showcase our model's ability to extract
and differentiate key features between human-written and ChatGPT-generated
text. Our findings provide important insights into the effective use of
language models to detect generated text.