arXiv AI
From Caricature to Collective Artifact: How Persona Construction Drives Polarization in Generative Social Simulations
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arXiv AI
2d ago
Validating LLM-generated political networks with survey-reported partisan exposure
The study evaluates large language models (LLMs) that generate political networks by comparing them to survey-reported partisan exposure from the ANES 2020–2022 Social Media Study. It finds that while network structure and political assortativity vary across model configurations, many models either over‑separate Democratic and Republican egos or underestimate Republican exposure, leading to group‑specific errors in local partisan environments. These discrepancies persist even among networks with similar aggregate properties, indicating that aggregate metrics alone can mask important individual‑level inaccuracies.
arXiv AI
Sep 1
How Identity and Opinion Shape Political Sycophancy in LLMs
arXiv:2608.29198v1 Announce Type: new Abstract: As Large Language Models (LLMs) increasingly encourage users to disclose personal profiles for tailored assistance, measuring their political alignment...
Hugging Face Trending Papers
6d ago
VoteGEO-Bench: Measuring the Impact of Generative Engine Optimization on Electoral Visibility
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...
arXiv Computation and Language
5d ago
VoteGEO-Bench: Measuring the Impact of Generative Engine Optimization on Electoral Visibility
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.
arXiv AI
Sep 15
Issue Bias in Generative AI Writing Assistance: Political Issues and LLMs in the Swedish 2026 Election
arXiv:2609.15207v1 Announce Type: new Abstract: Generative AI writing assistants and the Large Language Models (LLMs) that power them are increasingly part of how voters gather information before ele...
arXiv Computation and Language
Sep 1
Political Ideology Shifts in Large Language Models
arXiv:2508.16013v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed in politically sensitive contexts, raising concerns about their susceptibility to ideologica...