DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of
  Machine-Generated Text
Jinyan Su Terry Yue Zhuo Di Wang Preslav Nakov
With the rapid progress of large language models (LLMs) and the huge amount
of text they generated, it becomes more and more impractical to manually
distinguish whether a text is machine-generated. Given the growing use of LLMs
in social media and education, it prompts us to develop methods to detect
machine-generated text, preventing malicious usage such as plagiarism,
misinformation, and propaganda. Previous work has studied several zero-shot
methods, which require no training data. These methods achieve good
performance, but there is still a lot of room for improvement. In this paper,
we introduce two novel zero-shot methods for detecting machine-generated text
by leveraging the log rank information. One is called DetectLLM-LRR, which is
fast and efficient, and the other is called DetectLLM-NPR, which is more
accurate, but slower due to the need for perturbations. Our experiments on
three datasets and seven language models show that our proposed methods improve
over the state of the art by 3.9 and 1.75 AUROC points absolute. Moreover,
DetectLLM-NPR needs fewer perturbations than previous work to achieve the same
level of performance, which makes it more practical for real-world use. We also
investigate the efficiency--performance trade-off based on users preference on
these two measures and we provide intuition for using them in practice
effectively. We release the data and the code of both methods in
https://github.com/mbzuai-nlp/DetectLLM