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This research provides a rigorous mathematical analysis of attention mechanisms in large language models, specifically examining the geometric properties of bilinear forms within trained heads. By proving that symmetric components decompose into hyperbolic and zero forms for dense subsets of matrices, the study offers new theoretical insights into how LLMs structure their internal representations.
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On attention heads and bilinear forms

arXiv1 min read

arXiv:2609.22990v1 Announce Type: new Abstract: We study the symmetric and antisymmetric parts of bilinear forms in the attention heads of trained large language models. We introduce an orthogonally invariant profile map from real bilinear forms to a three-dimensional simplex and observe that profiles of trained bilinear forms accumulate near profiles of rank-one bilinear forms. We prove that the symmetric part of a bilinear form in an attention head is the sum of a hyperbolic form and a zero form for a Zariski-dense subset of query-key matrices.

Abstract:We study the symmetric and antisymmetric parts of bilinear forms in the attention heads of trained large language models. We introduce an orthogonally invariant profile map from real bilinear forms to a three-dimensional simplex and observe that profiles of trained bilinear forms accumulate near profiles of rank-one bilinear forms. We prove that the symmetric part of a bilinear form in an attention head is the sum of a hyperbolic form and a zero form for a Zariski-dense subset of query-key matrices.

Machine Learning (cs.LG); Spectral Theory (math.SP); Machine Learning (stat.ML)

arXiv:2609.22990 [cs.LG]

(or arXiv:2609.22990v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.22990 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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