通过频谱分析捕捉文本生成活力，提升无训练检测效果。
Unveiling Spectral Mechanisms in Training-Free LLM Text Detection
- 用频谱能量反映人类写作的波动特征，替代传统概率平均值
- 长文本连续生成时频域信号最强，短文本需结合其他视角
- 为多维度检测器设计提供实证依据，适合对抗生成内容
大语言模型的快速发展使得区分人工写作与机器生成文本愈发困难。无训练检测提供了可扩展的解决方案，但常见的基于置信度的指标主要衡量平均词元概率，常忽略体现人类写作特性的信号波动，我们称之为“生成活力”。频谱分析可捕捉这种活力，但其机制与实际边界仍不明确。本文从理论和实证角度分析频谱检测方法。我们发现频谱能量与代理对数概率轨迹的方差相关，并解释为何更广泛的词元选择会引发人类写作特有的波动，使频率域指标有效。进一步表明，该信号强度依赖于文本长度与采样范围：在长篇、连续、受限生成中频谱证据最清晰；而在短篇、碎片化、混合或编辑场景下，则需结合置信度与波动性视角。这些发现明确了频率域检测的适用条件，为未来多维检测器设计提供指导。
原文摘要 · Abstract (English)
The rapid advancement of Large Language Models (LLMs) makes it increasingly difficult to distinguish human writing from machine-generated text. Training-free detection offers a scalable solution, yet common confidence-based metrics mainly measure average token probabilities and often miss the signal fluctuations that characterize human writing, which we call "generative vitality". Spectral analysis offers a way to capture this vitality, but its mechanism and practical boundaries remain underexplored. In this paper, we analyze spectral detection from both theoretical and empirical perspectives. We connect spectral energy to variance in proxy log-probability trajectories and explain how broader human token choices create the fluctuations used by frequency-domain indicators. We further show that the strength of this signal depends on text length and sampling range: spectral evidence is clearest for long, continuous, constrained generation, while short, fragmented, mixed, and edited settings require complementary confidence and fluctuation views. These findings clarify when frequency-domain detection works and provide guidance for future multi-dimensional detector design.
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