Target-specified webpage rewriting raises a party's AI-answer exposure far more reliably than target-unspecified rewriting, in VoteGEO-Bench (Zhang & Xiao 2026)

GEOgenerative-engine-optimizationelectionsLLMinformation-retrievalarxiv

Zhang and Xiao's VoteGEO-Bench tests generative engine optimization (GEO) — rewriting a webpage to improve its visibility in AI-generated answers — against election queries from the 2024 U.S. presidential race and the 2026 Japanese House of Representatives election. They compare two rewriting conditions: "target-unspecified," where the rewriting model is not told which candidate or party to favor, and "target-specified," where it is told explicitly. Across 36 tested combinations of rewriting-strategy category (the four categories, not the eleven individual strategies), generative engine, and exposure metric, target-unspecified rewriting produced a significant positive gain in that party's measured exposure in only one setting, with four significant negative changes and the remaining 31 not significant. Target-specified rewriting, by contrast, produced significant positive gains in 24 of the 36 settings, including 10 of 12 for Share of Voice (SOV). Those are the uncorrected counts; the paper's own robustness appendix widens rather than narrows the gap between the two conditions — "For RQ1, 18 of 36 target-specified comparisons remain significant after clustering and correction, compared with 24 originally, while none of the 36 target-unspecified comparisons remain significant, compared with 5 originally."

The paper attributes the gap to how election content is actually written: in the U.S. data, 92% of the webpages in the benchmark's retrieval pools mention both major parties, so a rewrite given no stated target has no signal for which party's exposure it should be increasing. A rewrite that explicitly names its target removes that ambiguity and the gains follow.