Title: Provable Low-Frequency Bias of In-Context Learning of Representations
Primary area: interpretability and explainable AI
Keywords: In-Context Learning, Inference Dynamics, Representational Geometry

Abstract: In-context learning (ICL) enables large language models (LLMs) to acquire new behaviors from the input sequence alone without any parameter updates. Recent studies have shown that ICL can surpass the original meaning learned in the pretraining stage through internalizing the structure of the data-generating process (DGP) of the prompt into the hidden representations. However, the mechanisms by which LLMs achieve this ability are left open. In this paper, we present the first rigorous explanation of such phenomena by introducing a unified framework of double convergence, where hidden representations converge both over context and across layers. This double convergence process leads to an implicit bias towards smooth (low-frequency) representations, which we prove analytically and verify empirically. Our theory explains several open empirical observations, including why learned representations exhibit globally structured but locally distorted geometry, and why their total energy decays without vanishing. Moreover, our theory predicts that ICL has an intrinsic robustness towards high-frequency noise, which we empirically confirm. These results provide new insights into the underlying mechanisms of ICL, and a theoretical foundation to study it that hopefully extends to more general data distributions and settings.

TL;DR: We prove that in-context learning inherently biases the hidden representations of a LLM toward low-frequency structures, explaining global geometry, energy decay, and robustness to noise.