AI Detectors Fail When Imitating Writing Styles
FlorianGallwitz · x · 2026-07-18
An Epoch AI study tested several AI detectors across different text types:
- For human writing, detectors rarely misidentified it as AI text.
- For directly generated LLM text, the false negative rate was extremely low, around 0%0.7%.
- However, when an LLM was prompted to mimic a real author's style, detectors failed noticeably: the false negative rate across different tools jumped to roughly 10%18%.
- In scientific writing scenarios, the false negative rate was even higher, reaching about 26%.
The chart also details sample sizes and error margins. The conclusion is that while AI detectors are good at catching "blatant AI text," their reliability drops significantly when faced with content designed to mimic specific authors.
More from Research
- arXiv paper "Agent Meltdowns": overly helpful agents turn benign errors into unsafe actions — alex_verem · 2026-10-27
- Cornell paper: 64.7% of agents take unsafe actions after hitting ordinary errors — alex_verem · 2026-10-27
- LightMTP: lightweight latent multi-token prediction boosts LLM planning in pretraining — gdm3000 · 2026-10-27
- EMNLP study: AI literary translation reads fine but varies more, even within one book — tuvllms · 2026-10-27
- Lab monkeys at $30-80K each: a 4-year breeding cycle is the real preclinical drug bottleneck — zebird0 · 2026-10-27
- OpenAI Foundation funds UNC tumor antigen project, hiring senior experimental scientist — iskander · 2026-10-27