ChatGPT has become a global sensation. As ChatGPT and other Large Language Models (LLMs) emerge, concerns of misusing them in various ways increase, such as disseminating fake news, plagiarism, manipulating public opinion, cheating, and fraud. Hence, distinguishing AI-generated from human-generated becomes increasingly essential. Researchers have proposed various detection methodologies, ranging from basic binary classifiers to more complex deep-learning models. Some detection techniques rely on statistical characteristics or syntactic patterns, while others incorporate semantic or contextual information to improve accuracy. The primary objective of this study is to provide a comprehensive and contemporary assessment of the most recent techniques in ChatGPT detection. Additionally, we evaluated other AI-generated text detection tools that do not specifically claim to detect ChatGPT-generated content to assess their performance in detecting ChatGPT-generated content. For our evaluation, we have curated a benchmark dataset consisting of prompts from ChatGPT and humans, including diverse questions from medical, open Q&A, and finance domains and user-generated responses from popular social networking platforms. The dataset serves as a reference to assess the performance of various techniques in detecting ChatGPT-generated content. Our evaluation results demonstrate that none of the existing methods can effectively detect ChatGPT-generated content.
翻译：聊GPT已经成为全球轰动的现象。随着ChatGPT和其他大型语言模型（LLMs）的出现，人们越来越担心在各种方式的误用，如散布假新闻、抄袭、操纵公众意见、舞弊和欺诈。因此，区分AI生成的文本和人类生成的文本变得越来越重要。研究人员提议了各种检测方法，从基本的二进制分类器到更复杂的深度学习模型。一些检测技术依赖于统计特征或句法模式，而其他技术则包含语义或上下文信息以提高准确性。本研究的主要目标是综合评估最近的聊GPT检测技术。此外，我们还评估了其他不特别声称检测聊GPT生成内容的AI生成文本检测工具，以评估其在检测聊GPT生成内容方面的性能。为了评估我们的方法，我们准备了基准数据集，包括来自医学、开放问答和金融领域的各种问题以及来自流行社交网络平台的用户生成的响应。该数据集可以用作评估各种技术在检测聊GPT生成内容方面的性能的参考。我们的评估结果表明，目前不存在有效检测聊GPT生成内容的方法。
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