title: Detection Avoidance Techniques for Large Language Models
summary: 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.
paper_authors._id: 67cfa440aff9c98bb3f45a56
paper_authors.name: Sinclair Schneider
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2025-03-11T02:48:03.261Z
paper_authors._id: 64b3fc1fa24816979609dcb3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64b3fc1fa24816979609dcb3/cHRMs4YegRcgbZO8_bBaZ.jpeg
paper_authors.fullname: Sinclair Schneider
paper_authors.type: user
paper_authors.user: SinclairSchneider
paper_authors._id: 67cfa440aff9c98bb3f45a57
paper_authors.name: Florian Steuber
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T14:50:06.762Z
paper_authors._id: 64de10ee5e19298505465326
paper_authors.avatarUrl: /avatars/3ae7c6604bac70a6db62de08d08e8a2c.svg
paper_authors.fullname: Florian Steuber
paper_authors.type: user
paper_authors.user: steuber
paper_authors._id: 67cfa440aff9c98bb3f45a58
paper_authors.name: Joao A. G. Schneider
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-11T14:50:18.046Z
paper_authors._id: 64dadb5a311afacb53a7c2d0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64dadb5a311afacb53a7c2d0/bPH3B3B0WbADCS_k9ilAU.png
paper_authors.fullname: Joao Schneider
paper_authors.type: user
paper_authors.user: JoaoSchneider
paper_authors._id: 67cfa440aff9c98bb3f45a59
paper_authors.name: Gabi Dreo Rodosek
paper_title: Detection Avoidance Techniques for Large Language Models
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: large language models classification systems DetectGPT evasion techniques generative models temperature shallow learning fine-tuning reinforcement learning zero-shot detectors rephrasing