Detection Avoidance Techniques for Large Language Models
Authors: name: Sinclair Schneider, name: Florian Steuber, name: Joao A. G. Schneider, name: Gabi Dreo Rodosek

The increasing popularity of large language models has not only led to
widespread use but has also brought various risks, including the potential for
systematically spreading fake news. Consequently, the development of
classification systems such as DetectGPT has become vital. These detectors are
vulnerable to evasion techniques, as demonstrated in an experimental series:
Systematic changes of the generative models' temperature proofed shallow
learning-detectors to be the least reliable. Fine-tuning the generative model
via reinforcement learning circumvented BERT-based-detectors. Finally,
rephrasing led to a >90\% evasion of zero-shot-detectors like DetectGPT,
although texts stayed highly similar to the original. A comparison with
existing work highlights the better performance of the presented methods.
Possible implications for society and further research are discussed.