# Applying Ensemble Methods to Model-Agnostic Machine-Generated Text Detection

arXiv: https://arxiv.org/abs/2406.12570 · HTML: https://mlsanity.com/paper/2406.12570/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [Deep Neural Networks for Bot Detection](https://mlsanity.com/paper/1802.04289/) (2018)
- [To ChatGPT, or not to ChatGPT: That is the question!](https://mlsanity.com/paper/2304.01487/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Practical Synthesis of Detecting AI-Generated Textual, Visual, and Audio Content](https://mlsanity.com/paper/2504.02898/) (2025)
- [Domain Gating Ensemble Networks for AI-Generated Text Detection](https://mlsanity.com/paper/2505.13855/) (2025)

## Abstract (arXiv)

In this paper, we study the problem of detecting machine-generated text when the large language model (LLM) it is possibly derived from is unknown. We do so by apply ensembling methods to the outputs from DetectGPT classifiers (Mitchell et al. 2023), a zero-shot model for machine-generated text detection which is highly accurate when the generative (or base) language model is the same as the discriminative (or scoring) language model. We find that simple summary statistics of DetectGPT sub-model outputs yield an AUROC of 0.73 (relative to 0.61) while retaining its zero-shot nature, and that supervised learning methods sharply boost the accuracy to an AUROC of 0.94 but require a training dataset. This suggests the possibility of further generalisation to create a highly-accurate, model-agnostic machine-generated text detector.