Turbulence-like 5/3 spectral scaling in contextual representations of language as a complex system

1. 1Represent text as a trajectory in a high-dimensional embedding space using transformer-based language models
2. 2Quantify scale-dependent fluctuations along the token sequence using an embedding-step signal
3. 3Analyze the power spectrum of the signal to identify robust statistical regularities
4. 4Apply the findings to improve language model performance and efficiency

NLP researchers and AI engineers working on language models can benefit from this study to improve their understanding of language as a complex system, and apply this knowledge to develop more efficient and effective language models

💡 Language exhibits robust statistical regularities that can be quantified using spectral scaling, similar to turbulence in physical systems

📊 Turbulence-like scaling in language models! Researchers find 5/3 spectral scaling in contextual representations of language 🤖