The rapid advancement of large language models has raised significant concerns regarding their potential misuse by malicious actors. As a result, developing effective detectors to mitigate these risks has become a critical priority. However, most existing detection methods focus excessively on detection accuracy, often neglecting the societal risks posed by high false positive rates (FPRs). This paper addresses this issue by leveraging Conformal Prediction (CP), which effectively constrains the upper bound of FPRs. While directly applying CP constrains FPRs, it also leads to a significant reduction in detection performance. To overcome this trade-off, this paper proposes a Zero-Shot Machine-Generated Text Detection Framework via Multiscaled Conformal Prediction (MCP), which both enforces the FPR constraint and improves detection performance. This paper also introduces RealDet, a high-quality dataset that spans a wide range of domains, ensuring realistic calibration and enabling superior detection performance when combined with MCP. Empirical evaluations demonstrate that MCP effectively constrains FPRs, significantly enhances detection performance, and increases robustness against adversarial attacks across multiple detectors and datasets.

本研究针对现有文本检测方法过度关注检测准确性而忽视高虚假正例率（FPRs）带来的社会风险的问题，提出了一种通过多尺度符合预测（MCP）的零样本机器生成文本检测框架。该框架不仅有效限制了FPR的上限，还提升了检测性能，实证结果显示MCP在多个检测器和数据集上显著提升了检测性能，并增强了对对抗攻击的鲁棒性。

可靠界定虚假正例：通过多尺度符合预测的零样本机器生成文本检测框架