The Garden of Forking Paths: Observing Dynamic Parameters Distribution in Large Language Models
This addresses a foundational gap in mechanistic understanding of Transformer performance for NLP researchers, but it appears incremental as it builds on existing observations of parameter dynamics.
The paper tackles the problem of understanding why Transformers perform exceptionally well in NLP by examining how parameter distributions evolve during training, and it finds that observing bifurcation effects can explain model quality and reduce training costs, empirically supporting the effectiveness of weight sparsification.
A substantial gap persists in understanding the reasons behind the exceptional performance of the Transformer architecture in NLP. A particularly unexplored area involves the mechanistic description of how the distribution of parameters evolves over time during training. In this work we suggest that looking at the time evolution of the statistic distribution of model parameters, and specifically at bifurcation effects, can help understanding the model quality, potentially reducing training costs and evaluation efforts and empirically showing the reasons behind the effectiveness of weights sparsification.