Token-Ensemble Text Generation: On Attacking the Automatic AI-Generated Text Detection

• URL: http://arxiv.org/abs/2402.11167v1
• Date: Sat, 17 Feb 2024 02:25:57 GMT
• Title: Token-Ensemble Text Generation: On Attacking the Automatic AI-Generated Text Detection
• Authors: Fan Huang, Haewoon Kwak, Jisun An
• Abstract summary: This study proposes a novel token-ensemble generation strategy to challenge the robustness of current AI-content detection approaches. We find the token-ensemble approach significantly drops the performance of AI-content detection models.
• Score: 7.047135911489917
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The robustness of AI-content detection models against cultivated attacks (e.g., paraphrasing or word switching) remains a significant concern. This study proposes a novel token-ensemble generation strategy to challenge the robustness of current AI-content detection approaches. We explore the ensemble attack strategy by completing the prompt with the next token generated from random candidate LLMs. We find the token-ensemble approach significantly drops the performance of AI-content detection models (The code and test sets will be released). Our findings reveal that token-ensemble generation poses a vital challenge to current detection models and underlines the need for advancing detection technologies to counter sophisticated adversarial strategies.

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