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

• URL: http://arxiv.org/abs/2412.18274v1
• Date: Tue, 24 Dec 2024 08:33:44 GMT
• Title: GenAI Content Detection Task 2: AI vs. Human -- Academic Essay Authenticity Challenge
• Authors: Shammur Absar Chowdhury, Hind Almerekhi, Mucahid Kutlu, Kaan Efe Keles, Fatema Ahmad, Tasnim Mohiuddin, George Mikros, Firoj Alam,
• Abstract summary: The Academic Essay Authenticity Challenge was 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 challenge involves two languages: English and Arabic. This paper outlines the task formulation, details the dataset construction process, and explains the evaluation framework.
• Score: 12.076440946525434
• License:
• Abstract: 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.

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