Large language models (LLMs) such as GPT, Claude, Gemini, and Grok have been deeply integrated into our daily life. They now support a wide range of tasks -- from dialogue and email drafting to assisting with teaching and coding, serving as search engines, and much more. However, their ability to produce highly human-like text raises serious concerns, including the spread of fake news, the generation of misleading governmental reports, and academic misconduct. To address this practical problem, we train a classifier to determine whether a piece of text is authored by an LLM or a human. Our detector is deployed on an online CPU-based platform https://huggingface.co/spaces/stats-powered-ai/StatDetectLLM, and contains three novelties over existing detectors: (i) it does not rely on auxiliary information, such as watermarks or knowledge of the specific LLM used to generate the text; (ii) it more effectively distinguishes between human- and LLM-authored text; and (iii) it enables statistical inference, which is largely absent in the current literature. Empirically, our classifier achieves higher classification accuracy compared to existing detectors, while maintaining type-I error control, high statistical power, and computational efficiency.

翻译：诸如GPT、Claude、Gemini和Grok等大语言模型已深度融入我们的日常生活。它们目前支持广泛的任务——从对话和邮件起草到辅助教学与编程、充当搜索引擎等等。然而，其生成高度类人文本的能力引发了严重关切，包括虚假新闻传播、误导性政府报告生成以及学术不端行为。为应对这一实际问题，我们训练了一个分类器来判断给定文本是由大语言模型生成还是由人类撰写。我们的检测器部署于基于CPU的在线平台https://huggingface.co/spaces/stats-powered-ai/StatDetectLLM，并具备三项超越现有检测器的新特性：(i) 不依赖辅助信息，如水印或生成文本所用特定大语言模型的先验知识；(ii) 能更有效地区分人类撰写与大语言模型生成的文本；(iii) 支持统计推断功能，该功能在当前文献中基本缺失。实证结果表明，相较于现有检测器，我们的分类器在保持第一类错误控制、高统计功效和计算效率的同时，实现了更高的分类准确率。