Deep neural networks (DNNs) have shown much empirical success in solving
perceptual tasks across various cognitive modalities. While they are only
loosely inspired by the biological brain, recent studies report considerable
similarities between representations extracted from task-optimized DNNs and
neural populations in the brain. DNNs have subsequently become a popular model
class to infer computational principles underlying complex cognitive functions,
and in turn, they have also emerged as a natural testbed for applying methods
originally developed to probe information in neural populations. In this work,
we utilize mean-field theoretic manifold analysis, a recent technique from
computational neuroscience that connects geometry of feature representations
with linear separability of classes, to analyze language representations from
large-scale contextual embedding models. We explore representations from
different model families (BERT, RoBERTa, GPT, etc.) and find evidence for
emergence of linguistic manifolds across layer depth (e.g., manifolds for
part-of-speech tags), especially in ambiguous data (i.e, words with multiple
part-of-speech tags, or part-of-speech classes including many words). In
addition, we find that the emergence of linear separability in these manifolds
is driven by a combined reduction of manifolds' radius, dimensionality and
inter-manifold correlations.

该研究使用均场理论流形分析来分析来自大规模上下文嵌入模型的语言表示，发现在不同的模型家族中出现了语言流形的证据，尤其是在多义词和包含许多单词的词性类别中，此外，发现这些流形中的线性可分性的出现是由流形半径、维度和流形间相关性的共同减少所驱动的。