Spotlights and Blindspots: Evaluating Machine-Generated Text Detection
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 cert...
| Authors | Kevin Stowe, Kailash Patil |
|---|---|
| Primary category | cs.CL |
| Published | 17 April 2026 |
| Last updated at source | 22 April 2026 |
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