# GenAI Content Detection Task 2: AI vs. Human -- Academic Essay Authenticity Challenge

arXiv: https://arxiv.org/abs/2412.18274 · HTML: https://mlsanity.com/paper/2412.18274/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 37 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 17 of 17)

- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [GPT detectors are biased against non-native English writers](https://mlsanity.com/paper/2304.02819/) (2023)
- [RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors](https://mlsanity.com/paper/2405.07940/) (2024)
- [The Science of Detecting LLM-Generated Texts](https://mlsanity.com/paper/2303.07205/) (2023)
- [M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection](https://mlsanity.com/paper/2305.14902/) (2023)
- [DAWN: Dynamic Adversarial Watermarking of Neural Networks](https://mlsanity.com/paper/1906.00830/) (2019)
- [Testing of Detection Tools for AI-Generated Text](https://mlsanity.com/paper/2306.15666/) (2023)
- [CHEAT: A Large-scale Dataset for Detecting ChatGPT-writtEn AbsTracts](https://mlsanity.com/paper/2304.12008/) (2023)
- [Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites](https://mlsanity.com/paper/2305.09820/) (2023)
- [ConDA: Contrastive Domain Adaptation for AI-generated Text Detection](https://mlsanity.com/paper/2309.03992/) (2023)
- [GenQA: Generating Millions of Instructions from a Handful of Prompts](https://mlsanity.com/paper/2406.10323/) (2024)
- [SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection](https://mlsanity.com/paper/2404.14183/) (2024)
- [Unsupervised and Distributional Detection of Machine-Generated Text](https://mlsanity.com/paper/2111.02878/) (2021)
- [Distinguishing ChatGPT(-3.5, -4)-generated and human-written papers through Japanese stylometric analysis](https://mlsanity.com/paper/2304.05534/) (2023)
- [GenAI Detection Tools, Adversarial Techniques and Implications for Inclusivity in Higher Education](https://mlsanity.com/paper/2403.19148/) (2024)

## Built on by (most cited first, 6 of 6)

- [Human-LLM Coevolution: Evidence from Academic Writing](https://mlsanity.com/paper/2502.09606/) (2025)
- [GenAI Content Detection Task 3: Cross-Domain Machine-Generated Text Detection Challenge](https://mlsanity.com/paper/2501.08913/) (2025)
- [Robust and Fine-Grained Detection of AI Generated Texts](https://mlsanity.com/paper/2504.11952/) (2025)
- [MultiAIGCD: A Comprehensive dataset for AI Generated Code Detection Covering Multiple Languages, Models,Prompts, and Scenarios](https://mlsanity.com/paper/2507.21693/) (2025)
- [IntegrityAI at GenAI Detection Task 2: Detecting Machine-Generated Academic Essays in English and Arabic Using ELECTRA and Stylometry](https://mlsanity.com/paper/2501.05476/) (2025)
- [Automatic detection of Gen-AI texts: A comparative framework of neural models](https://mlsanity.com/paper/2603.18750/) (2026)

## Abstract (arXiv)

This paper presents a comprehensive overview of the first edition of the Academic Essay Authenticity Challenge, organized as part of the GenAI Content Detection shared tasks collocated with COLING 2025. This challenge focuses on detecting machine-generated vs. human-authored essays for academic purposes. The task is defined as follows: "Given an essay, identify whether it is generated by a machine or authored by a human.'' The challenge involves two languages: English and Arabic. During the evaluation phase, 25 teams submitted systems for English and 21 teams for Arabic, reflecting substantial interest in the task. Finally, seven teams submitted system description papers. The majority of submissions utilized fine-tuned transformer-based models, with one team employing Large Language Models (LLMs) such as Llama 2 and Llama 3. This paper outlines the task formulation, details the dataset construction process, and explains the evaluation framework. Additionally, we present a summary of the approaches adopted by participating teams. Nearly all submitted systems outperformed the n-gram-based baseline, with the top-performing systems achieving F1 scores exceeding 0.98 for both languages, indicating significant progress in the detection of machine-generated text.