Since ChatGPT has emerged as a major AIGC model, providing high-quality responses across a wide range of applications (including software development and maintenance), it has attracted much interest from many individuals. ChatGPT has great promise, but there are serious problems that might arise from its misuse, especially in the realms of education and public safety. Several AIGC detectors are available, and they have all been tested on genuine text. However, more study is needed to see how effective they are for multi-domain ChatGPT material. This study aims to fill this need by creating a multi-domain dataset for testing the state-of-the-art APIs and tools for detecting artificially generated information used by universities and other research institutions. A large dataset consisting of articles, abstracts, stories, news, and product reviews was created for this study. The second step is to use the newly created dataset to put six tools through their paces. Six different artificial intelligence (AI) text identification systems, including "GPTkit," "GPTZero," "Originality," "Sapling," "Writer," and "Zylalab," have accuracy rates between 55.29 and 97.0%. Although all the tools fared well in the evaluations, originality was particularly effective across the board.

翻译：自ChatGPT作为主要AIGC模型出现以来，它在广泛的应用领域（包括软件开发和维护）中提供高质量响应，吸引了众多个人的极大兴趣。ChatGPT前景广阔，但其滥用可能引发严重问题，尤其是在教育和公共安全领域。目前已有多种AIGC检测工具问世，且均已在真实文本上经过测试。然而，这些工具在多领域ChatGPT内容上的有效性仍需进一步研究。本研究旨在通过创建多领域数据集来填补这一空白，以测试高校及其他研究机构用于检测人工生成信息的最新API和工具。我们构建了包含文章、摘要、故事、新闻和产品评论在内的大规模数据集。第二步是利用新创建的数据集对六种工具进行性能评估。六种不同的人工智能文本识别系统，包括"GPTkit"、"GPTZero"、"Originality"、"Sapling"、"Writer"和"Zylalab"，准确率介于55.29%至97.0%之间。尽管所有工具在评估中表现良好，但Originality在各类测试中尤为有效。