With the advancement of generative language models, the generated text has
come remarkably close to high-quality human-authored text in terms of fluency
and diversity. This calls for a highly practical detection tool that can
identify the source of text, preferably pinpointing the language model it
originates from. However, existing detection tools typically require access to
language models and can only differentiate between machine-generated and
human-authored text, failing to meet the requirements of rapid detection and
text tracing. Therefore, in this paper, we propose an efficient, secure, and
scalable detection tool called LLMDet, which calculates the proxy perplexity of
text by utilizing the prior information of the model's next-token
probabilities, obtained through pre-training. Subsequently, we use the
self-watermarking information of the model, as measured by proxy perplexity, to
detect the source of the text. We found that our method demonstrates impressive
detection performance while ensuring speed and security, particularly achieving
a recognition accuracy of 97.97\% for human-authored text. Furthermore, our
detection tool also shows promising results in identifying the large language
model (e.g., GPT-2, OPT, LLaMA, Vicuna...) responsible for the text. We release
the code and processed data at https://github.com/TrustedLLM/LLMDet.

本文提出了一种高效、安全、可扩展的检测工具 LLMDet，通过利用预训练获得的模型下一个令牌概率的先验信息，计算文本的代理困惑度，并使用通过代理困惑度测量的模型的自身水印信息来检测文本的来源；同时，在识别人类撰写的文本方面实现了 97.97% 的识别准确率，并在识别生成语言模型方面显示出了很好的结果。