  The rampant proliferation of large language models, fluent enough to generate
text indistinguishable from human-written language, gives unprecedented
importance to the detection of machine-generated text. This work is motivated
by an important research question: How will the detectors of machine-generated
text perform on outputs of a new generator, that the detectors were not trained
on? We begin by collecting generation data from a wide range of LLMs, and train
neural detectors on data from each generator and test its performance on
held-out generators. While none of the detectors can generalize to all
generators, we observe a consistent and interesting pattern that the detectors
trained on data from a medium-size LLM can zero-shot generalize to the larger
version. As a concrete application, we demonstrate that robust detectors can be
built on an ensemble of training data from medium-sized models.
