The paper examines how Preference Inference (PI) models used in large-scale participatory democracy platforms can alter the perceived consensus and minority support by predicting missing votes. It introduces a collective‑centric evaluation framework that assesses whether inferred votes maintain key properties of the overall preference landscape, rather than focusing solely on individual prediction accuracy. Using the largest multilingual dataset to date—four consultations with over 90,000 participants, 1 million votes, and 22 languages—the study finds that models with similar predictive accuracy can differ markedly in how well they preserve the collective structure, underscoring that accuracy alone is insufficient for evaluating PI in democratic contexts.
By Pierre-Antoine Lequeu, Salim Hafid, Paul Lerner, Nazanin Shafiabadi, Laur\`ene Cave, David Mas, Jean-Philippe Cointet, Benjamin Piwowarski, Fran\c{c}ois Yvon
arXiv:2605. 01642v2 Announce Type: replace Abstract: Prevailing alignment methods target a fixed set of preferences and therefore risk forcing value lock-in as societal norms evolve over time.
By Rachel Freedman
arXiv:2606. 12754v1 Announce Type: cross Abstract: Are large language models (LLMs) bad at capturing human judgment?
By Danica Dillion, Chen Cecilia Liu, Baihui Wang, Daniele Barolo, Tanmay Rajore, Niket Tandon, Pranathi Ravikumar, Kurt Gray
VoteGEO-Bench is a benchmark that examines how generative engine optimization (GEO) affects the visibility of election-related web content in large language model responses. It covers the 2024 U.S. presidential election and the 2026 Japanese House of Representatives election, using election queries, historical webpages, and voter personas. The study evaluates 11 rewriting strategies, finding that target‑specified rewrites and those that improve page organization or strengthen arguments increase exposure more than other tactics, while partisan identities can create larger baseline exposure gaps than a single rewritten page.
By Shiqi Zhang, Xiaokui Xiao
arXiv:2608. 15871v1 Announce Type: cross Abstract: Large language models (LLMs) trained on large-scale internet corpora encode extensive statistical regularities about social identities, attitudes, and political behaviour.
By Roman Neruda, Martin Bako\v{s}, Josef \v{S}lerka, V\'it Tu\v{c}ek, Petra Vidnerov\'a, Gabriela Kadlecov\'a
Large language models (LLMs) are increasingly used for election information, yet little is known about how external web content shapes the political information they present. Generative engine optimiz...