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Your Language Model Can Secretly Write Like Humans: Contrastive Paraphrase Attacks on LLM-Generated Text Detectors
Created by
Haebom
Author
Hao Fang, Jiawei Kong, Tianqu Zhuang, Yixiang Qiu, Kuofeng Gao, Bin Chen, Shu-Tao Xia, Yaowei Wang, Min Zhang
Outline
This paper proposes a novel attack technique, the Contrastive Paraphrase Attack (CoPA), to bypass detectors that detect text generated by large-scale language models (LLMs). Existing methods require extensive data and computing resources to train specialized paraphrasers, and their effectiveness is significantly reduced when compared to advanced detection algorithms. CoPA effectively tricks text detectors without training by leveraging existing LLMs. While LLMs carefully craft instructions to generate human-like text, their inherent statistical biases can leave behind machine-like features. Therefore, CoPA utilizes machine-like word distributions as a control. By subtracting machine-like patterns from the human-like distribution during the decoding process, CoPA generates sentences that are difficult for the detector to detect. The effectiveness of CoPA is verified through theoretical analysis and experiments.
Takeaways, Limitations
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Takeaways:
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A novel approach is presented that overcomes the limitations of existing paraphrasing-based attack methods (requiring large amounts of data and computing resources, and reducing effectiveness against advanced detection algorithms).
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Proposing an efficient attack method that requires no training.
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Increase attack success rate by taking into account the unique statistical bias of LLM.
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Experimentally verifying the effectiveness of deceiving text detectors in various scenarios.
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Limitations:
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Further research is needed on the long-term effectiveness and sustainability of the proposed CoPA (in response to the emergence of new detection algorithms).
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Complete elimination of statistical bias in LLMs may be difficult. Further research is needed to determine how to completely remove residual mechanical characteristics.
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Consider ethical issues: Discuss the potential misuse of technologies like CoPA.
