Feature-Augmented Transformers for Robust AI-Text Detection Across Domains and Generators
Making AI-text detectors work reliably across different sources and writing styles
Detectors trained to spot AI-generated text perform near-perfectly on familiar material but fail badly when encountering text from new sources or generators—a problem researchers call brittleness. Adding linguistic features like readability and vocabulary patterns to a transformer model improved performance across different domains, pushing balanced accuracy from around 60% to 86% when tested on unfamiliar text.
As AI systems generate text at scale across the internet, platforms need detectors that actually work in the real world, not just in controlled testing. This research shows that simple feature engineering can make detectors three times more reliable when encountering new types of AI generators, making them practically useful for content moderation and detection systems that can't be retrained constantly.