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.
| Original language | English |
|---|---|
| Pages (from-to) | 1-9 |
| Number of pages | 9 |
| Journal | International Conference Recent Advances in Natural Language Processing, RANLP |
| DOIs | |
| State | Published - 2025 |
| Event | 9th Student Research Workshop, RANLPStud 2025 - Varna, Bulgaria Duration: 8 Sep 2025 → 10 Sep 2025 |
Bibliographical note
Publisher Copyright:© 2025 Incoma Ltd. All rights reserved.
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Artificial Intelligence
- Electrical and Electronic Engineering
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