Theory-Grounded Evaluation Exposes the Authorship Gap in LLM Personalization

Abstract:Stylistic personalization - making LLMs write in a specific individual's style, rather than merely adapting to task preferences - lacks evaluation grounded in authorship science. We show that grounding evaluation in authorship verification theory transforms what benchmarks can measure. Drawing on three measurement traditions - LUAR (a trained authorship verification model), an LLM-as-judge with decoupled trait matching, and classical function-word stylometrics - we evaluate four inference-time personalization methods across 50 authors and 1,000 generations. The theory-grounded metric (LUAR) provides what ad hoc alternatives cannot: calibrated baselines (human ceiling 0.756, cross-author floor 0.626) that give scores absolute meaning. All methods score below this floor (0.484-0.508), exposing an authorship gap invisible to uncalibrated metrics. The three metrics produce near-zero pairwise correlations (|r| < 0.07), confirming that without theoretical grounding, metric choice determines conclusions - an LLM judge declares a clear winner while LUAR finds no meaningful differentiation. These findings demonstrate the theory-benchmark cycle in action: authorship theory exposes evaluation failures that ad hoc benchmarks miss.

Computation and Language (cs.CL)

arXiv:2604.26460 [cs.CL]

(or arXiv:2604.26460v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2604.26460 Focus to learn more arXiv-issued DOI via DataCite

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