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.

大型语言模型的广泛发展使得检测机器生成的文本变得前所未有的重要。本研究通过收集大范围的语言模型生成数据，训练神经检测器并测试其在未经训练的生成器输出上的性能，发现中等规模语言模型训练的检测器能够零样本推广到更大的版本。作为一个具体的应用，我们证明了可以在中等规模模型的训练数据集合上构建强大的检测器。