AH&AITD – Arslan’s Human and AI Text Database

AH&AITD is a comprehensive benchmark dataset designed to support the evaluation of AI-generated text detection tools. The dataset contains 11,580 samples spanning both human-written and AI-generated content across multiple domains. It was developed to address limitations in previous datasets, particularly in terms of diversity, scale, and real-world applicability. To facilitate research in the detection of AI-generated text by providing a diverse, multi-domain dataset. This dataset enables fair benchmarking of detection tools across various writing styles and content categories.Composition1. Human-Written Samples (Total: 5,790)Collected from:Open Web Text (2,343 samples)Blogs (196 samples)Web Text (397 samples)Q&A Platforms (670 samples)News Articles (430 samples)Opinion Statements (1,549 samples)Scientific Research Abstracts (205 samples)2. AI-Generated Samples (Total: 5,790)Generated using:ChatGPT (1,130 samples)GPT-4 (744 samples)Paraphrase Models (1,694 samples)GPT-2 (328 samples)GPT-3 (296 samples)DaVinci (GPT-3.5 variant) (433 samples)GPT-3.5 (364 samples)OPT-IML (406 samples)Flan-T5 (395 samples)Citation:Akram, A. (2023). AH&AITD: Arslan’s Human and AI Text Database. [Dataset]. Associated with the article: An Empirical Study of AI-Generated Text Detection Tools. Advances in Machine Learning & Artificial Intelligence, 4(2), 44–55.