The remarkable capabilities of large-scale language models, such as ChatGPT, in text generation have impressed readers and spurred researchers to devise detectors to mitigate potential risks, including misinformation, phishing, and academic dishonesty. Despite this, most previous studies have been predominantly geared towards creating detectors that differentiate between purely ChatGPT-generated texts and human-authored texts. This approach, however, fails to work on discerning texts generated through human-machine collaboration, such as ChatGPT-polished texts. Addressing this gap, we introduce a novel dataset termed HPPT (ChatGPT-polished academic abstracts), facilitating the construction of more robust detectors. It diverges from extant corpora by comprising pairs of human-written and ChatGPT-polished abstracts instead of purely ChatGPT-generated texts. Additionally, we propose the "Polish Ratio" method, an innovative measure of the degree of modification made by ChatGPT compared to the original human-written text. It provides a mechanism to measure the degree of ChatGPT influence in the resulting text. Our experimental results show our proposed model has better robustness on the HPPT dataset and two existing datasets (HC3 and CDB). Furthermore, the "Polish Ratio" we proposed offers a more comprehensive explanation by quantifying the degree of ChatGPT involvement.

翻译：大规模语言模型（如ChatGPT）在文本生成方面的卓越能力给读者留下了深刻印象，并促使研究人员开发检测器以减轻潜在风险，包括错误信息、网络钓鱼和学术不端行为。尽管如此，以往的大多数研究主要侧重于创建区分纯ChatGPT生成文本与人类撰写文本的检测器。然而，这种方法无法有效识别通过人机协作生成的文本，例如经ChatGPT润色的文本。为弥补这一不足，我们引入了一个名为HPPT（ChatGPT润色学术摘要）的新数据集，用于构建更稳健的检测器。与现有语料库不同，该数据集包含人类撰写与ChatGPT润色摘要的配对样本，而非纯ChatGPT生成的文本。此外，我们提出了“润色比例”方法，这是一种创新性的度量方式，用于衡量ChatGPT相较于原始人类撰写文本的修改程度。该方法提供了一种机制，可量化结果文本中ChatGPT的影响程度。实验结果表明，我们提出的模型在HPPT数据集以及两个现有数据集（HC3和CDB）上具有更强的鲁棒性。此外，我们提出的“润色比例”通过量化ChatGPT的参与程度，提供了更全面的解释。