Detecting LLM-Generated Text with Performance Guarantees

• URL: http://arxiv.org/abs/2601.06586v1
• Date: Sat, 10 Jan 2026 14:52:45 GMT
• Title: Detecting LLM-Generated Text with Performance Guarantees
• Authors: Hongyi Zhou, Jin Zhu, Ying Yang, Chengchun Shi,
• Abstract summary: Large language models (LLMs) such as GPT, Claude, Gemini, and Grok have been deeply integrated into our daily life.<n>They now support a wide range of tasks -- from dialogue and email drafting to assisting with teaching and coding.<n>Their ability to produce highly human-like text raises serious concerns, including the spread of fake news.
• Score: 13.29284903739996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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