SILO: Revealing the Low Rank Structure of Language Models through Sequences of Logits
Abstract: A major problem in the study of large language models, and deep learning more broadly, is to understand their inherent low-dimensional structure. We introduce an approach to study the low-dimensional structure of language models at a model-agnostic level: as sequential probabilistic models. We first empirically demonstrate that a wide …