Information Entropy for LLM-generated Text Detection

Abstract

With the explosive growth of text generated by large language models (LLMs) on the internet, technologies for detecting LLM-generated text are increasingly important. However, existing technologies often require frequent access to LLMs during detection, significantly restricting their application in scenarios where only the text is accessible. To tackle this challenge, we identify that human-written texts exhibit higher entropy than LLM-generated texts. Motivated by this, we propose a novel method named IED, which leverages the Information Entropy for generative text Detection. Specifically, IED first transforms the given text into a vector of which each dimension represents the information entropy of each word, and then adopts a classifier to conduct the detection. Further, we propose IEGD which adopts the information gain to construct the vector by enhancing the differentiation between human-written and LLM-generated texts. To evaluate the effective of the proposed method, we construct a new large-scale dataset comprised of generated texts using various LLMs (LLaMA-7B, GPT-NeoX, etc.). Extensive experiments show that the proposed methods achieve improvement over state-of-the-art methods by up to 10.81%.