Comparing the Humanness of Machine-Generated and Human-Authored Text

As chatbots have become more commonplace writing tools, a need exists to understand the breadth of research about the humanness of machine-generated text via techniques that extend beyond the traditional Turing Test, in both dialogue (e.g., conversing with a chatbot) and non-dialogue (e.g., reading a news article) scenarios. To fill this gap and support future work, we survey current literature that examines and identifies humanness features of written communication generated with the state-of-the-art generative pre-trained transformer language models, provide a working definition of humanness, propose a text-based humanness taxonomy based on linguistic properties, and identify current research gaps.