arXiv cs.AI· Xinpeng Wang, Bruce Rushing, Zhizhen Chen, Zhe Zeng·· 12 小时前AI 评分31
利用局部熵波动检测黑盒 LLM 生成文本的 RelPPR 指标
Local Entropy Fluctuations for Black-Box LLM-Generated Text Detection
AI 导读
研究者提出 Relative Peak Prominence Rate（RelPPR）指标，通过捕捉机器生成文本沿熵轨迹出现的尖锐局部熵波动，实现黑盒场景下的 LLM 生成文本检测。现有双模型检测器和重采样检测器在指令条件下的文本上会严重失效，RelPPR 在多种模型家族与数据集上取得 SOTA 黑盒检测性能，并在多种实际场景中保持最可靠。代码已公开。
正文
Abstract:Zero-shot detection of machine-generated text is vital for mitigating the risks of large language models (LLMs) misuse. While the state-of-the-art dual-model detectors and resampling-based detectors outperform single-model statistical detectors on unprompted continuations, they suffer a catastrophic breakdown on instruction-conditioned text in the black-box setting, where the source model is unknown. We analyze this failure and trace it to poor prompt conditioning for resampling-based methods and diminished cross-model discrepancy for dual-model detectors. Motivated by recent studies that human writing maintains a steady, uniform information flow, whereas machine-generated text exhibits sharp, localized entropy fluctuations, we propose a new metric, the Relative Peak Prominence Rate (RelPPR) to capture this structural contrast along the entropy trajectory and further leverage it as a signal for LLM-generated text detection. Extensive evaluations across diverse model families and datasets demonstrate that RelPPR achieves state-of-the-art black-box detection performance and remains the most reliable detector under various practical situations. Our code is available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.33767 [cs.AI] |
| (or arXiv:2610.33767v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33767 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xinpeng Wang [view email]
[v1]
Sat, 24 Oct 2026 16:18:20 UTC (913 KB)
来源：arXiv cs.AI · arxiv.org