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arXiv AI By Xinpeng Wang, Bruce Rushing, Zhizhen Chen, Zhe Zeng

Local Entropy Fluctuations for Black-Box LLM-Generated Text Detection

Read the original on arXiv AI →

The paper introduces a new metric, Relative Peak Prominence Rate (RelPPR), to detect black-box LLM-generated text by measuring localized entropy fluctuations. It shows that existing dual-model and resampling-based detectors fail on instruction-conditioned text, and that RelPPR outperforms them across diverse models and datasets. The authors provide code for their detector on GitHub.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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