用计量学方法分析文本语义演化，发现人类与模型的稳定性差异。
Scale-Dependent Semantic Dynamics Revealed by Allan Deviation
- 将句子嵌入视为高维空间中的随机轨迹，用阿拉安偏差分析语义稳定性。
- 人类文本在短时呈现幂律变化，长时趋于稳定噪声底限，模型则稳定性更弱。
- 揭示语义连贯性可量化，适合研究认知机制与大模型差异的人看。
语言在语义状态序列中演进，但其内在动态仍不明确。本文将书面文本的语义进展视为高维状态空间中的随机轨迹，利用精密计量学中的阿拉安偏差工具，将有序句向量视为位移信号，分析意义的稳定性。分析揭示两种不同动力学范式：短时间呈现幂律标度，区分了创意文学与技术文本；长时间则过渡到受限制的稳定性噪声底限。尽管大语言模型能模仿人类文本的局部标度统计特性，但其稳定性时间范围系统性缩短。这些结果将语义连贯性确立为可测量的物理属性，为区分人类认知与算法生成模式的细微动态提供了框架。
原文摘要 · Abstract (English)
While language progresses through a sequence of semantic states, the underlying dynamics of this progression remain elusive. Here, we treat the semantic progression of written text as a stochastic trajectory in a high-dimensional state space. We utilize Allan deviation, a tool from precision metrology, to analyze the stability of meaning by treating ordered sentence embeddings as a displacement signal. Our analysis reveals two distinct dynamical regimes: short-time power-law scaling, which differentiates creative literature from technical texts, and a long-time crossover to a stability-limited noise floor. We find that while large language models successfully mimic the local scaling statistics of human text, they exhibit a systematic reduction in their stability horizon. These results establish semantic coherence as a measurable physical property, offering a framework to differentiate the nuanced dynamics of human cognition from the patterns generated by algorithmic models.
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