# A Comprehensive Dataset for Human vs. AI Generated Text Detection

arXiv: https://arxiv.org/abs/2510.22874 · HTML: https://mlsanity.com/paper/2510.22874/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 58 references that are in the map.
- First posted on arXiv 26 Oct 2025.

## Builds on (most cited first, 30 of 39)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature](https://mlsanity.com/paper/2310.05130/) (2023)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2401.12070/) (2024)
- [RADAR: Robust AI-Text Detection via Adversarial Learning](https://mlsanity.com/paper/2307.03838/) (2023)
- [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://mlsanity.com/paper/1909.03242/) (2019)
- [RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors](https://mlsanity.com/paper/2405.07940/) (2024)
- [Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks](https://mlsanity.com/paper/2001.06362/) (2020)
- [M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection](https://mlsanity.com/paper/2305.14902/) (2023)
- [TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation](https://mlsanity.com/paper/2109.13296/) (2021)
- [GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification](https://mlsanity.com/paper/1908.01843/) (2019)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection](https://mlsanity.com/paper/1911.03854/) (2019)
- [Graph Neural Networks with Continual Learning for Fake News Detection from Social Media](https://mlsanity.com/paper/2007.03316/) (2020)
- [GERE: Generative Evidence Retrieval for Fact Verification](https://mlsanity.com/paper/2204.05511/) (2022)
- [DetectRL: Benchmarking LLM-Generated Text Detection in Real-World Scenarios](https://mlsanity.com/paper/2410.23746/) (2024)
- [LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection](https://mlsanity.com/paper/2408.04284/) (2024)
- [Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMs](https://mlsanity.com/paper/2408.12060/) (2024)
- [Factify 2: A Multimodal Fake News and Satire News Dataset](https://mlsanity.com/paper/2304.03897/) (2023)
- [Findings of Factify 2: Multimodal Fake News Detection](https://mlsanity.com/paper/2307.10475/) (2023)

## Built on by (most cited first, 3 of 3)

- [ECLIPTICA -- A Framework for Switchable LLM Alignment via CITA - Contrastive Instruction-Tuned Alignment](https://mlsanity.com/paper/2601.06157/) (2026)
- [Latent Trajectory Discrimination for AI-Generated Text Detection](https://mlsanity.com/paper/2607.14967/) (2026)
- [Team DACTYL at PAN 2026: Bayesian Data Mixing and Empirical X-risk Minimization for AI-text Detection](https://mlsanity.com/paper/2607.17382/) (2026)

## Abstract (arXiv)

The rapid advancement of large language models (LLMs) has led to increasingly human-like AI-generated text, raising concerns about content authenticity, misinformation, and trustworthiness. Addressing the challenge of reliably detecting AI-generated text and attributing it to specific models requires large-scale, diverse, and well-annotated datasets. In this work, we present a comprehensive dataset comprising over 73,193 text samples that combine authentic New York Times articles with synthetic versions generated by multiple state-of-the-art LLMs including Gemma-2-9b, Mistral-7B, Qwen-2-72B, LLaMA-8B, Yi-Large, and GPT-4-o. The dataset provides original article abstracts as prompts, full human-authored narratives. We establish baseline results for two key tasks: distinguishing human-written from AI-generated text, achieving an accuracy of 58.35%, and attributing AI texts to their generating models with an accuracy of 8.92%. By bridging real-world journalistic content with modern generative models, the dataset aims to catalyze the development of robust detection and attribution methods, fostering trust and transparency in the era of generative AI. Our dataset is available at: https://huggingface.co/datasets/Rajarshi-Roy-research/DefactifyTextDataset