@inproceedings{lamsiyah-etal-2025-daigt,
title = "{M}-{DAIGT}: A Shared Task on Multi-Domain Detection of {AI}-Generated Text",
author = "Lamsiyah, Salima and
Ezzini, Saad and
El Mahdaouy, Abdelkader and
Alami, Hamza and
Benlahbib, Abdessamad and
El amrany, Samir and
Chafik, Salmane and
Hammouchi, Hicham",
editor = "Lamsiyah, Salima and
Ezzini, Saad and
El Mahdaoui, Abdelkader and
Alami, Hamza and
Benlahbib, Abdessamad and
El Amrani, Samir and
Chafik, Salmane and
Hammouchi, Hicham",
booktitle = "Proceedings of the Shared Task on Multi-Domain Detection of AI-Generated Text",
month = sep,
year = "2025",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2025.ranlp-mdaigt.1/",
pages = "1--9",
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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%0 Conference Proceedings
%T M-DAIGT: A Shared Task on Multi-Domain Detection of AI-Generated Text
%A Lamsiyah, Salima
%A Ezzini, Saad
%A El Mahdaouy, Abdelkader
%A Alami, Hamza
%A Benlahbib, Abdessamad
%A El amrany, Samir
%A Chafik, Salmane
%A Hammouchi, Hicham
%Y Lamsiyah, Salima
%Y Ezzini, Saad
%Y El Mahdaoui, Abdelkader
%Y Alami, Hamza
%Y Benlahbib, Abdessamad
%Y El Amrani, Samir
%Y Chafik, Salmane
%Y Hammouchi, Hicham
%S Proceedings of the Shared Task on Multi-Domain Detection of AI-Generated Text
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F lamsiyah-etal-2025-daigt
%X 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.
%U https://aclanthology.org/2025.ranlp-mdaigt.1/
%P 1-9
Markdown (Informal)
[M-DAIGT: A Shared Task on Multi-Domain Detection of AI-Generated Text](https://aclanthology.org/2025.ranlp-mdaigt.1/) (Lamsiyah et al., RANLP 2025)
ACL
- Salima Lamsiyah, Saad Ezzini, Abdelkader El Mahdaouy, Hamza Alami, Abdessamad Benlahbib, Samir El amrany, Salmane Chafik, and Hicham Hammouchi. 2025. M-DAIGT: A Shared Task on Multi-Domain Detection of AI-Generated Text. In Proceedings of the Shared Task on Multi-Domain Detection of AI-Generated Text, pages 1–9, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.