Papers › Linguistic Collapse: Neural Collapse in (Large) Language Models

Linguistic Collapse: Neural Collapse in (Large) Language Models

28 May 2024arXiv:2405.17767archive 2025-07-28

Robert Wu, Vardan Papyan

Neural collapse (𝒩𝒞) is a phenomenon observed in classification tasks where top-layer representations collapse into their class means, which become equinorm, equiangular and aligned with the classifiers. These behaviours -- associated with generalization and robustness -- would manifest under specific conditions: models are trained towards zero loss, with noise-free labels belonging to balanced classes, which do not outnumber the model's hidden dimension. Recent studies have explored 𝒩𝒞 in the absence of one or more of these conditions to extend and capitalize on the associated benefits of ideal geometries. Language modelling presents a curious frontier, as \textit{training by token prediction} constitutes a classification task where none of the conditions exist: the vocabulary is imbalanced and exceeds the embedding dimension; different tokens might correspond to similar contextual embeddings; and large language models (LLMs) in particular are typically only trained for a few epochs. This paper empirically investigates the impact of scaling the architectures and training of causal language models (CLMs) on their progression towards 𝒩𝒞. We find that 𝒩𝒞 properties that develop with scale (and regularization) are linked to generalization. Moreover, there is evidence of some relationship between 𝒩𝒞 and generalization independent of scale. Our work thereby underscores the generality of 𝒩𝒞 as it extends to the novel and more challenging setting of language modelling. Downstream, we seek to inspire further research on the phenomenon to deepen our understanding of LLMs -- and neural networks at large -- and improve existing architectures based on 𝒩𝒞-related properties. Our code is hosted on GitHub at https://github.com/rhubarbwu/linguistic-collapse .

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