# GPT-generated Text Detection: Benchmark Dataset and Tensor-based Detection Method

arXiv: https://arxiv.org/abs/2403.07321 · HTML: https://mlsanity.com/paper/2403.07321/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 12 references that are in the map.
- First posted on arXiv 12 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings](https://mlsanity.com/paper/1804.09088/) (2018)

## Built on by (most cited first, 8 of 8)

- [Is Your Paper Being Reviewed by an LLM? Benchmarking AI Text Detection in Peer Review](https://mlsanity.com/paper/2502.19614/) (2025)
- [Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection](https://mlsanity.com/paper/2506.02350/) (2025)
- [Jailbreaking Leaves a Trace: Understanding and Detecting Jailbreak Attacks from Internal Representations of Large Language Models](https://mlsanity.com/paper/2602.11495/) (2026)
- [Do Internal Layers of LLMs Reveal Patterns for Jailbreak Detection?](https://mlsanity.com/paper/2510.06594/) (2025)
- [Did ChatGPT or Copilot use alter the style of internet news headlines? A time series regression analysis](https://mlsanity.com/paper/2503.23811/) (2025)
- [CoCoTen: Detecting Adversarial Inputs to Large Language Models through Latent Space Features of Contextual Co-occurrence Tensors](https://mlsanity.com/paper/2508.02997/) (2025)
- [M-DAIGT: A Shared Task on Multi-Domain Detection of AI-Generated Text](https://mlsanity.com/paper/2511.11340/) (2025)
- [Large Language Models and Forensic Linguistics: Navigating Opportunities and Threats in the Age of Generative AI](https://mlsanity.com/paper/2512.06922/) (2025)

## Abstract (arXiv)

As natural language models like ChatGPT become increasingly prevalent in applications and services, the need for robust and accurate methods to detect their output is of paramount importance. In this paper, we present GPT Reddit Dataset (GRiD), a novel Generative Pretrained Transformer (GPT)-generated text detection dataset designed to assess the performance of detection models in identifying generated responses from ChatGPT. The dataset consists of a diverse collection of context-prompt pairs based on Reddit, with human-generated and ChatGPT-generated responses. We provide an analysis of the dataset's characteristics, including linguistic diversity, context complexity, and response quality. To showcase the dataset's utility, we benchmark several detection methods on it, demonstrating their efficacy in distinguishing between human and ChatGPT-generated responses. This dataset serves as a resource for evaluating and advancing detection techniques in the context of ChatGPT and contributes to the ongoing efforts to ensure responsible and trustworthy AI-driven communication on the internet. Finally, we propose GpTen, a novel tensor-based GPT text detection method that is semi-supervised in nature since it only has access to human-generated text and performs on par with fully-supervised baselines.