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arXiv · 2405.15471

Emergence of a High-Dimensional Abstraction Phase in Language Transformers

Emily Cheng·Diego Doimo·Corentin Kervadec·Iuri Macocco·Jade Yu·Alessandro Laio·Marco Baroni
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Abstract

A language model (LM) is a mapping from a linguistic context to an output token. However, much remains to be known about this mapping, including how its geometric properties relate to its function. We take a high-level geometric approach to its analysis, observing, across five pre-trained transformer-based LMs and three input datasets, a distinct phase characterized by high intrinsic dimensionality. During this phase, representations (1) correspond to the first full linguistic abstraction of the input; (2) are the first to viably transfer to downstream tasks; (3) predict each other across different LMs. Moreover, we find that an earlier onset of the phase strongly predicts better language modelling performance. In short, our results suggest that a central high-dimensionality phase underlies core linguistic processing in many common LM architectures.

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Emily Cheng, Diego Doimo, Corentin Kervadec, Iuri Macocco, Jade Yu, Alessandro Laio, Marco Baroni. 2024-05-24. Emergence of a High-Dimensional Abstraction Phase in Language Transformers. https://arxiv.org/abs/2405.15471

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