[Submitted on 4 Nov 2026]
Title:Consistent yet Contextual: Learning Author-Level Mixtures of Style for Authorship Attribution
View a PDF of the paper titled Consistent yet Contextual: Learning Author-Level Mixtures of Style for Authorship Attribution, by Omkar Yadav and 4 other authors
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Abstract:Approaches to authorship verification or attribution typically model the style of a document, asking whether two documents were likely written by the same author. While recent approaches have been performant, such approaches largely ignore modeling the authors themselves and how varied their style is. Here, we introduce multiple alternatives for modeling the author and demonstrate that allowing an author's representation to capture multiple consistent but distinct writing styles yields significant performance improvements. Using our new authorship model, we then examine when an author writes in a way that is most idiomatic of their style across four scenarios: (i) variation in the size of their writing audiences, (ii) temporal distance from when the model was trained, (iii) the effect of preceding context, and (iv) the effect of genre norms on style. Across all four experiments, we demonstrate that authors vary substantially in how idiolectic their writing is. Our results have clear implications for both the modeling and data used for authorship verification and attribution, showing that authors need to be represented with their multiple styles and that some settings produce text that is much more informative about who wrote that text.
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2611.07111 [cs.CL]
(or arXiv:2611.07111v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2611.07111
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: David Jurgens [view email] [v1] Wed, 4 Nov 2026 20:25:47 UTC (187 KB)
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