# Cat and Mouse -- Can Fake Text Generation Outpace Detector Systems?

arXiv: https://arxiv.org/abs/2506.21274 · HTML: https://mlsanity.com/paper/2506.21274/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [Ghostbuster: Detecting Text Ghostwritten by Large Language Models](https://mlsanity.com/paper/2305.15047/) (2023)
- [Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods](https://mlsanity.com/paper/2210.07321/) (2022)
- [How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inference](https://mlsanity.com/paper/2505.09598/) (2025)
- [Comparative Analysis Based on DeepSeek, ChatGPT, and Google Gemini: Features, Techniques, Performance, Future Prospects](https://mlsanity.com/paper/2503.04783/) (2025)
- [Using Machine Learning to Distinguish Human-written from Machine-generated Creative Fiction](https://mlsanity.com/paper/2412.15253/) (2024)

## Abstract (arXiv)

Large language models can produce convincing "fake text" in domains such as academic writing, product reviews, and political news. Many approaches have been investigated for the detection of artificially generated text. While this may seem to presage an endless "arms race", we note that newer LLMs use ever more parameters, training data, and energy, while relatively simple classifiers demonstrate a good level of detection accuracy with modest resources. To approach the question of whether the models' ability to beat the detectors may therefore reach a plateau, we examine the ability of statistical classifiers to identify "fake text" in the style of classical detective fiction. Over a 0.5 version increase, we found that Gemini showed an increased ability to generate deceptive text, while GPT did not. This suggests that reliable detection of fake text may remain feasible even for ever-larger models, though new model architectures may improve their deceptiveness