  Large language models (LLMs) have demonstrated remarkable capability to
generate fluent responses to a wide variety of user queries. However, this has
also raised concerns about the potential misuse of such texts in journalism,
education, and academia. In this study, we strive to create automated systems
that can detect machine-generated texts and pinpoint potential misuse. We first
introduce a large-scale benchmark \textbf{M4}, which is a multi-generator,
multi-domain, and multi-lingual corpus for machine-generated text detection.
Through an extensive empirical study of this dataset, we show that it is
challenging for detectors to generalize well on instances from unseen domains
or LLMs. In such cases, detectors tend to misclassify machine-generated text as
human-written. These results show that the problem is far from solved and that
there is a lot of room for improvement. We believe that our dataset will enable
future research towards more robust approaches to this pressing societal
problem. The dataset is available at https://github.com/mbzuai-nlp/M4.
