A Metric-Driven Evaluation of Rephrased and Generated Texts

Abstract

The rapid development of large language models has raised serious concerns about the reliability of detecting content created by artificial intelligence. This article compares the stylistic metrics of texts generated using a multimodal model and an autoregressive model. The results show that the generated text is very similar to human-written text in terms of lexical diversity and semantic coherence. In terms of perplexity and burstiness, the rephrased texts are practically indistinguishable from the original human-written texts, which leads to a high level of false negatives in autoregressive detectors. Our analysis highlights the need for new detection methods and suggests further directions, including more specific stylometric signatures. Relying solely on a single stylometric metric leads to unreliable differentiation between generated and human-written text. © 2025 Copyright for this paper by its authors.