GPT-Sentinel：區分人類和ChatGPT生成的內容

GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content

May 13, 2023

作者: Yutian Chen, Hao Kang, Vivian Zhai, Liangze Li, Rita Singh, Bhiksha Ramakrishnan

cs.AI

摘要

本文提出了一種新穎的方法，用於使用語言模型檢測ChatGPT生成的文本與人類撰寫的文本。為此，我們首先收集並釋出了一個名為OpenGPTText的預處理數據集，其中包含使用ChatGPT生成的重新編寫內容。然後，我們設計、實施並訓練了兩種不同的文本分類模型，分別使用了Robustly Optimized BERT Pretraining Approach（RoBERTa）和Text-to-Text Transfer Transformer（T5）。我們的模型取得了卓越的結果，在測試數據集上的準確率超過了97％，通過各種指標進行評估。此外，我們進行了一項可解釋性研究，展示了我們的模型提取和區分人類撰寫和ChatGPT生成文本之間的關鍵特徵的能力。我們的研究結果提供了有關有效使用語言模型來檢測生成文本的重要見解。

English

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.