Detecting LLM-Generated Texts with "Classical" Machine Learning
This article is currently an experimental machine translation and may contain errors. If anything is unclear, please refer to the original Chinese version. I am continuously working to improve the translation.
As of early 2026, mainstream LLM-generated text exhibits strong statistical patterns that can be effectively distinguished from human-written content using traditional machine learning models. I suspect this is how many so-called “AI plagiarism checkers” actually work under the hood.
Online Demo: https://lyc8503.github.io/AITextDetector/
The model used in this demo is not trained on general-purpose data , nor has it undergone rigorous optimization or iteration. Its single-sentence detection accuracy is approximately 85% on the test set. Please read through this article before use to understand potential limitations.
The core code (drafts) and trained model files are available on GitHub: lyc8503/AITextDetector
Back when I was still writing my thesis at school half a year ago, rumors were already spreading about checking papers for AIGC (AI-generated content). I tested several platforms—CNKI, Wanfang, and some third-party AIGC detection services—and found they could indeed distinguish between my hand-written text and LLM-generated text with decent accuracy.