Large language models (LLMs) have demonstrated remarkable capability to
generate fluent responses to a wide variety of user queries, but this has also
resulted in concerns regarding the potential misuse of such texts in
journalism, educational, and academic context. In this work, we aim to develop
automatic systems to identify machine-generated text and to detect potential
misuse. We first introduce a large-scale benchmark M4, which is
multi-generator, multi-domain, and multi-lingual corpus for machine-generated
text detection. Using the dataset, we experiment with a number of methods and
we show that it is challenging for detectors to generalize well on unseen
examples if they are either from different domains or are generated by
different large language models. In such cases, detectors tend to misclassify
machine-generated text as human-written. These results show that the problem is
far from solved and there is a lot of room for improvement. We believe that our
dataset M4, which covers different generators, domains and languages, will
enable future research towards more robust approaches for this pressing
societal problem. The M4 dataset is available at
this https URL

本文介绍了一个大规模的文本检测数据集 M4，并利用该数据集实验了多种方法，发现在不同领域或用不同的大型语言模型生成的文本中，模型检测器倾向于将机器生成的文本误分类为人工书写的文本，并指出解决此问题仍有很多待改进的空间，M4 数据集将为以后研究提供优质的数据支持。