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Due to the rapid development of text generation models, people increasinglyoften encounter texts that may start out as written by a human but thencontinue as machine-generated results of large language models. Detecting theboundary between human-written and machine-generated parts of such texts is avery challenging problem that has not received much attention in literature. Inthis work, we consider and compare a number of different approaches for thisartificial text boundary detection problem, comparing several predictors overfeatures of different nature. We show that supervised fine-tuning of theRoBERTa model works well for this task in general but fails to generalize inimportant cross-domain and cross-generator settings, demonstrating a tendencyto overfit to spurious properties of the data. Then, we propose novelapproaches based on features extracted from a frozen language model'sembeddings that are able to outperform both the human accuracy level andpreviously considered baselines on the Real or Fake Text benchmark. Moreover,we adapt perplexity-based approaches for the boundary detection task andanalyze their behaviour. We analyze the robustness of all proposed classifiersin cross-domain and cross-model settings, discovering important properties ofthe data that can negatively influence the performance of artificial textboundary detection algorithms.

Artificial Text Boundary Detection with Topological Data Analysis and Sliding Window Techniques