title: GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content
summary: 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.
paper_authors._id: 6462dfc1960c80d19c66d43d
paper_authors.name: Yutian Chen
paper_authors._id: 6462dfc1960c80d19c66d43e
paper_authors.name: Hao Kang
paper_authors._id: 6462dfc1960c80d19c66d43f
paper_authors.name: Vivian Zhai
paper_authors._id: 6462dfc1960c80d19c66d440
paper_authors.name: Liangze Li
paper_authors._id: 6462dfc1960c80d19c66d441
paper_authors.name: Rita Singh
paper_authors._id: 6462dfc1960c80d19c66d442
paper_authors.name: Bhiksha Ramakrishnan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-16T15:32:01.825Z
paper_authors._id: 644898876ffed6ece102a856
paper_authors.avatarUrl: /avatars/eb5df5b95d8f2adc70056fe71e7b4e1e.svg
paper_authors.fullname: Bhiksha Raj
paper_authors.type: user
paper_authors.user: bhiksha
paper_title: GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content
paper_summary: 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.
paper_ai_keywords: Robustly Optimized BERT Pretraining Approach (RoBERTa) Text-to-Text Transfer Transformer (T5) interpretability study