M-DAIGT: A Shared Task on Multi-Domain Detection of AI-Generated Text

TL;DR

This paper introduces M-DAIGT, a shared task and benchmark dataset for detecting AI-generated text across news and academic domains, highlighting current approaches and future challenges.

Contribution

It presents a new large-scale dataset and a shared task for multi-domain AI-generated text detection, fostering research in this critical area.

Findings

Four teams participated in the shared task.

The dataset includes 30,000 samples from various LLMs.

Methods varied across teams, indicating diverse approaches.

Abstract

The generation of highly fluent text by Large Language Models (LLMs) poses a significant challenge to information integrity and academic research. In this paper, we introduce the Multi-Domain Detection of AI-Generated Text (M-DAIGT) shared task, which focuses on detecting AI-generated text across multiple domains, particularly in news articles and academic writing. M-DAIGT comprises two binary classification subtasks: News Article Detection (NAD) (Subtask 1) and Academic Writing Detection (AWD) (Subtask 2). To support this task, we developed and released a new large-scale benchmark dataset of 30,000 samples, balanced between human-written and AI-generated texts. The AI-generated content was produced using a variety of modern LLMs (e.g., GPT-4, Claude) and diverse prompting strategies. A total of 46 unique teams registered for the shared task, of which four teams submitted final results. All four teams participated in both Subtask 1 and Subtask 2. We describe the methods employed by these participating teams and briefly discuss future directions for M-DAIGT.