Mastering Syntax, Unlocking Semantics: A Mathematically Provable Two-stage Learning Process in Transformers
Keywords: Two-stage learning, Optimization dynamics, Feature learning theory
TL;DR: Rigorously prove that transformers follow a syntax-then-semantics learning paradigm
Abstract: Transformers have emerged as a cornerstone across various fields with extensive applications.
However, the training dynamics of transformers remain relatively underexplored.
In this work, we present a novel perspective on how transformers acquire knowledge during the training dynamics, inspired by the feature learning theory.
To this end, we conceptualize each token as embodying two types of knowledge: elementary knowledge represented by syntactic information, and specialized knowledge represented by semantic information.
Building on this data structure, we rigorously prove that transformers follow a syntax-then-semantics learning paradigm, i.e., first mastering syntax in the Elementary Stage and then unlocking semantics in the subsequent Specialized Stage.
The results are derived from the training dynamics analysis and finite-time convergence within the in-context learning framework for supervised classification.
To our best knowledge, this is the \textbf{\emph{first}} rigorous result of a two-stage optimization process in transformers from a feature learning perspective. Empirical findings on real-world language datasets support the theoretical results of the two-stage learning process.
Moreover, the spectral properties of attention weights, derived from our theoretical framework, align with the experimental observations, providing further validation.
Primary Area: learning theory
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Submission Number: 7086
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