Detection Avoidance Techniques for Large Language Models
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.
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Sinclair Schneider
extracted_confirmed
2025-03-11T02:48:03.261Z
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Sinclair Schneider
user
SinclairSchneider
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Florian Steuber
admin_assigned
2025-03-11T14:50:06.762Z
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Florian Steuber
user
steuber
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Joao A. G. Schneider
admin_assigned
2025-03-11T14:50:18.046Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64dadb5a311afacb53a7c2d0/bPH3B3B0WbADCS_k9ilAU.png
Joao Schneider
user
JoaoSchneider
67cfa440aff9c98bb3f45a59
Gabi Dreo Rodosek
Detection Avoidance Techniques for Large Language Models
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.
Classification systems like DetectGPT to identify large language model-generated content are susceptible to evasion techniques, including temperature changes, reinforcement learning fine-tuning, and rephrasing.
large language models classification systems DetectGPT evasion techniques generative models temperature shallow learning fine-tuning reinforcement learning zero-shot detectors rephrasing