Token-Ensemble Text Generation: On Attacking the Automatic AI-Generated Text Detection
<|reference_start|>Token-Ensemble Text Generation: On Attacking the Automatic AI-Generated Text Detection: 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.<|reference_end|>
@article{huang2024toblend:,
    title={ToBlend: Token-Level Blending With an Ensemble of LLMs to Attack
  AI-Generated Text Detection},
    author={Fan Huang, Haewoon Kwak, Jisun An},
    journal={arXiv preprint arXiv:2402.11167},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.11167},
    primaryClass={cs.CL cs.AI}
}