The widespread accessibility of large language models (LLMs) to the general public has significantly amplified the dissemination of machine-generated texts (MGTs). Advancements in prompt manipulation have exacerbated the difficulty in discerning the origin of a text (human-authored vs machinegenerated). This raises concerns regarding the potential misuse of MGTs, particularly within educational and academic domains. In this paper, we present $\textbf{LLM-DetectAIve}$ -- a system designed for fine-grained MGT detection. It is able to classify texts into four categories: human-written, machine-generated, machine-written machine-humanized, and human-written machine-polished. Contrary to previous MGT detectors that perform binary classification, introducing two additional categories in LLM-DetectiAIve offers insights into the varying degrees of LLM intervention during the text creation. This might be useful in some domains like education, where any LLM intervention is usually prohibited. Experiments show that LLM-DetectAIve can effectively identify the authorship of textual content, proving its usefulness in enhancing integrity in education, academia, and other domains. LLM-DetectAIve is publicly accessible at https://huggingface.co/spaces/raj-tomar001/MGT-New. The video describing our system is available at https://youtu.be/E8eT_bE7k8c.

翻译：大型语言模型（LLM）的广泛普及显著加剧了机器生成文本（MGT）的传播。提示词操控技术的进步使得辨别文本来源（人类撰写与机器生成）变得愈发困难。这引发了人们对MGT潜在滥用的担忧，尤其是在教育和学术领域。本文提出$\textbf{LLM-DetectAIve}$——一个专为细粒度MGT检测设计的系统。它能够将文本分类为四个类别：人类撰写、机器生成、机器撰写后人工润色，以及人类撰写后机器润色。与以往执行二元分类的MGT检测器不同，LLM-DetectAIve引入的两个额外类别有助于深入理解文本创作过程中LLM干预的不同程度。这在某些领域（如通常禁止任何LLM干预的教育领域）可能具有实用价值。实验表明，LLM-DetectAIve能够有效识别文本内容的作者身份，证明了其在提升教育、学术及其他领域诚信方面的实用性。LLM-DetectAIve已在https://huggingface.co/spaces/raj-tomar001/MGT-New公开访问。描述我们系统的视频可在https://youtu.be/E8eT_bE7k8c观看。