  In this paper, we study the problem of detecting machine-generated text when
the large language model (LLM) it is possibly derived from is unknown. We do so
by apply ensembling methods to the outputs from DetectGPT classifiers (Mitchell
et al. 2023), a zero-shot model for machine-generated text detection which is
highly accurate when the generative (or base) language model is the same as the
discriminative (or scoring) language model. We find that simple summary
statistics of DetectGPT sub-model outputs yield an AUROC of 0.73 (relative to
0.61) while retaining its zero-shot nature, and that supervised learning
methods sharply boost the accuracy to an AUROC of 0.94 but require a training
dataset. This suggests the possibility of further generalisation to create a
highly-accurate, model-agnostic machine-generated text detector.
