M-DAIGT: A Shared Task on Multi-Domain Detection of AI-Generated Text
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914158550712320 |
|---|---|
| author | Lamsiyah, Salima Ezzini, Saad Mahdaouy, Abdelkader El Alami, Hamza Benlahbib, Abdessamad Amrany, Samir El Chafik, Salmane Hammouchi, Hicham |
| author_facet | Lamsiyah, Salima Ezzini, Saad Mahdaouy, Abdelkader El Alami, Hamza Benlahbib, Abdessamad Amrany, Samir El Chafik, Salmane Hammouchi, Hicham |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_11340 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | M-DAIGT: A Shared Task on Multi-Domain Detection of AI-Generated Text Lamsiyah, Salima Ezzini, Saad Mahdaouy, Abdelkader El Alami, Hamza Benlahbib, Abdessamad Amrany, Samir El Chafik, Salmane Hammouchi, Hicham Computation and Language Artificial Intelligence 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. |
| title | M-DAIGT: A Shared Task on Multi-Domain Detection of AI-Generated Text |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2511.11340 |