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

arXiv: https://arxiv.org/abs/2511.11340 · HTML: https://mlsanity.com/paper/2511.11340/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 43 references that are in the map.
- First posted on arXiv 14 Nov 2025.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [Can AI-Generated Text be Reliably Detected?](https://mlsanity.com/paper/2303.11156/) (2023)
- [Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://mlsanity.com/paper/1911.00650/) (2019)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [Protecting Language Generation Models via Invisible Watermarking](https://mlsanity.com/paper/2302.03162/) (2023)
- [RankGen: Improving Text Generation with Large Ranking Models](https://mlsanity.com/paper/2205.09726/) (2022)
- [Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text?](https://mlsanity.com/paper/2308.01284/) (2023)
- [Feature-Level Insights into Artificial Text Detection with Sparse Autoencoders](https://mlsanity.com/paper/2503.03601/) (2025)
- [HACo-Det: A Study Towards Fine-Grained Machine-Generated Text Detection under Human-AI Coauthoring](https://mlsanity.com/paper/2506.02959/) (2025)
- [Enhancing Handwritten Text Recognition with N-gram sequence decomposition and Multitask Learning](https://mlsanity.com/paper/2012.14459/) (2020)
- [Iron Sharpens Iron: Defending Against Attacks in Machine-Generated Text Detection with Adversarial Training](https://mlsanity.com/paper/2502.12734/) (2025)
- [TextDefense: Adversarial Text Detection based on Word Importance Entropy](https://mlsanity.com/paper/2302.05892/) (2023)
- [Stress-testing Machine Generated Text Detection: Shifting Language Models Writing Style to Fool Detectors](https://mlsanity.com/paper/2505.24523/) (2025)
- [GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method](https://mlsanity.com/paper/2403.07321/) (2024)
- [Detection of Human and Machine-Authored Fake News in Urdu](https://mlsanity.com/paper/2410.19517/) (2024)
- [Mitigating Paraphrase Attacks on Machine-Text Detectors via Paraphrase Inversion](https://mlsanity.com/paper/2410.21637/) (2024)
- [TrustAI at SemEval-2024 Task 8: A Comprehensive Analysis of Multi-domain Machine Generated Text Detection Techniques](https://mlsanity.com/paper/2403.16592/) (2024)
- [Who Writes What: Unveiling the Impact of Author Roles on AI-generated Text Detection](https://mlsanity.com/paper/2502.12611/) (2025)
- [Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion](https://mlsanity.com/paper/2506.15981/) (2025)

## Built on by (most cited first, 2 of 2)

- [GATech at AbjadGenEval Shared Task: Multilingual Embeddings for Arabic Machine-Generated Text Classification](https://mlsanity.com/paper/2603.10007/) (2026)
- [AEyeDE: An Attention-Based Attribution Framework for AI-Generated Text Detection](https://mlsanity.com/paper/2606.00016/) (2026)

## Abstract (arXiv)

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.