  Due to the rapid development of large language models, people increasingly
often encounter texts that may start as written by a human but continue as
machine-generated. Detecting the boundary between human-written and
machine-generated parts of such texts is a challenging problem that has not
received much attention in literature. We attempt to bridge this gap and
examine several ways to adapt state of the art artificial text detection
classifiers to the boundary detection setting. We push all detectors to their
limits, using the Real or Fake text benchmark that contains short texts on
several topics and includes generations of various language models. We use this
diversity to deeply examine the robustness of all detectors in cross-domain and
cross-model settings to provide baselines and insights for future research. In
particular, we find that perplexity-based approaches to boundary detection tend
to be more robust to peculiarities of domain-specific data than supervised
fine-tuning of the RoBERTa model; we also find which features of the text
confuse boundary detection algorithms and negatively influence their
performance in cross-domain settings.
