  Large language models (LLMs) have shown the ability to produce fluent and
cogent content, presenting both productivity opportunities and societal risks.
To build trustworthy AI systems, it is imperative to distinguish between
machine-generated and human-authored content. The leading zero-shot detector,
DetectGPT, showcases commendable performance but is marred by its intensive
computational costs. In this paper, we introduce the concept of conditional
probability curvature to elucidate discrepancies in word choices between LLMs
and humans within a given context. Utilizing this curvature as a foundational
metric, we present **Fast-DetectGPT**, an optimized zero-shot detector, which
substitutes DetectGPT's perturbation step with a more efficient sampling step.
Our evaluations on various datasets, source models, and test conditions
indicate that Fast-DetectGPT not only surpasses DetectGPT by a relative around
75% in both the white-box and black-box settings but also accelerates the
detection process by a factor of 340, as detailed in Table 1. See
\url{https://github.com/baoguangsheng/fast-detect-gpt} for code, data, and
results.
