Spotlights and Blindspots: Evaluation Machine-Generated Text Detection
Provides a comprehensive empirical comparison for researchers and practitioners choosing machine-generated text detectors, highlighting the critical impact of evaluation methodology.
Evaluated 15 detection models across 7 test sets and 3 human-written datasets, finding no single system excels in all areas and performance varies greatly with dataset and metric choices.
With the rise of generative language models, machine-generated text detection has become a critical challenge. A wide variety of models is available, but inconsistent datasets, evaluation metrics, and assessment strategies obscure comparisons of model effectiveness. To address this, we evaluate 15 different detection models from six distinct systems, as well as seven trained models, across seven English-language textual test sets and three creative human-written datasets. We provide an empirical analysis of model performance, the influence of training and evaluation data, and the impact of key metrics. We find that no single system excels in all areas and nearly all are effective for certain tasks, and the representation of model performance is critically linked to dataset and metric choices. We find high variance in model ranks based on datasets and metrics, and overall poor performance on novel human-written texts in high-risk domains. Across datasets and metrics, we find that methodological choices that are often assumed or overlooked are essential for clearly and accurately reflecting model performance.