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About this paper
Zhongxin Yang, Chun Bao, Yuanwei Bin, Xiang I.A. Yang, Shiyi Chen
This research reveals a consistent 5/3 power-law scaling in the power spectra of token-to-token embedding differences from transformer-based language models across multiple languages and text sources. This spectral signature, reminiscent of the Kolmogorov spectrum in fluid turbulence, indicates a scale-free organization of semantic information within contextual representations.
Key insights
- The observed 5/3 power-law scaling in contextual embeddings suggests that semantic information is integrated in a scale-free, self-similar manner across linguistic scales, drawing an analogy to the Kolmogorov spectrum in fluid turbulence.
- The identified scaling is a fundamental property of context-dependent, sequentially ordered language representations, emerging progressively in deeper transformer layers as contextual information is integrated.
- This reproducible spectral fingerprint offers a model-agnostic characteristic for understanding and potentially benchmarking the multiscale structural integrity of language representations, independent of specific tasks or surface-level lexical statistics.
Summary by alphaXiv.
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