LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection

TL;DR

LLM-DetectAIve is a novel system that classifies texts into four categories, including human-written, machine-generated, obfuscated machine-generated, and polished human texts, addressing the need for fine-grained detection of AI-generated content.

Contribution

It introduces a multi-class detection system capable of distinguishing nuanced categories of AI and human texts, surpassing binary classification approaches.

Findings

Effective identification of four text categories.

Supports detection of obfuscated machine-generated texts.

Useful in educational and academic contexts.

Abstract

The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written or machine-generated. This raises concerns about potential misuse, particularly within educational and academic domains. Thus, it is important to develop practical systems that can automate the process. Here, we present one such system, LLM-DetectAIve, designed for fine-grained detection. Unlike most previous work on machine-generated text detection, which focused on binary classification, LLM-DetectAIve supports four categories: (i) human-written, (ii) machine-generated, (iii) machine-written, then machine-humanized, and (iv) human-written, then machine-polished. Category (iii) aims to detect attempts to obfuscate the fact that a text was machine-generated, while category (iv) looks for cases where the LLM was used to polish a human-written text, which is typically acceptable in academic writing, but not in education. Our experiments show that LLM-DetectAIve can effectively identify the above four categories, which makes it a potentially useful tool in education, academia, and other domains. LLM-DetectAIve is publicly accessible at https://github.com/mbzuai-nlp/LLM-DetectAIve. The video describing our system is available at https://youtu.be/E8eT_bE7k8c.