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

Salima Lamsiyah, Saad Ezzini, Abdelkader El Mahdaouy, Hamza Alami, Abdessamad Benlahbib +3 more
11/14/2025
cs.CLcs.AI

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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.

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Cite this paper

@article{lamsiyah2025mdaigt,
  title  = {M-DAIGT: A Shared Task on Multi-Domain Detection of AI-Generated Text},
  author = {Salima Lamsiyah and Saad Ezzini and Abdelkader El Mahdaouy and Hamza Alami and Abdessamad Benlahbib and Samir El Amrany and Salmane Chafik and Hicham Hammouchi},
  year   = {2025},
  eprint = {2511.11340},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2511.11340v1}
}

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