  As LLMs rapidly advance, increasing concerns arise regarding risks about
actual authorship of texts we see online and in real world. The task of
distinguishing LLM-authored texts is complicated by the nuanced and overlapping
behaviors of both machines and humans. In this paper, we challenge the current
practice of considering LLM-generated text detection a binary classification
task of differentiating human from AI. Instead, we introduce a novel ternary
text classification scheme, adding an "undecided" category for texts that could
be attributed to either source, and we show that this new category is crucial
to understand how to make the detection result more explainable to lay users.
This research shifts the paradigm from merely classifying to explaining
machine-generated texts, emphasizing need for detectors to provide clear and
understandable explanations to users. Our study involves creating four new
datasets comprised of texts from various LLMs and human authors. Based on new
datasets, we performed binary classification tests to ascertain the most
effective SOTA detection methods and identified SOTA LLMs capable of producing
harder-to-detect texts. We constructed a new dataset of texts generated by two
top-performing LLMs and human authors, and asked three human annotators to
produce ternary labels with explanation notes. This dataset was used to
investigate how three top-performing SOTA detectors behave in new ternary
classification context. Our results highlight why "undecided" category is much
needed from the viewpoint of explainability. Additionally, we conducted an
analysis of explainability of the three best-performing detectors and the
explanation notes of the human annotators, revealing insights about the
complexity of explainable detection of machine-generated texts. Finally, we
propose guidelines for developing future detection systems with improved
explanatory power.
