  Neural text detectors are models trained to detect whether a given text was
generated by a language model or written by a human. In this paper, we
investigate three simple and resource-efficient strategies (parameter tweaking,
prompt engineering, and character-level mutations) to alter texts generated by
GPT-3.5 that are unsuspicious or unnoticeable for humans but cause
misclassification by neural text detectors. The results show that especially
parameter tweaking and character-level mutations are effective strategies.
