Tootfinder

Political Ideology Shifts in Large Language Models
Pietro Bernardelle, Stefano Civelli, Leon Fr\"ohling, Riccardo Lunardi, Kevin Roitero, Gianluca Demartini
https://arxiv.org/abs/2508.16013

Political Ideology Shifts in Large Language Models
Large language models (LLMs) are increasingly deployed in politically sensitive settings, raising concerns about their potential to encode, amplify, or be steered toward specific ideologies. We investigate how adopting synthetic personas influences ideological expression in LLMs across seven models (7B-70B+ parameters) from multiple families, using the Political Compass Test as a standardized probe. Our analysis reveals four consistent patterns: (i) larger models display broader and more polari…

The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models
Konrad L\"ohr, Shuzhou Yuan, Michael F\"arber
https://arxiv.org/abs/2510.08236

The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models
Large Language Models (LLMs) are increas- ingly integral to information dissemination and decision-making processes. Given their grow- ing societal influence, understanding potential biases, particularly within the political domain, is crucial to prevent undue influence on public opinion and democratic processes. This work investigates political bias and stereotype propa- gation across eight prominent LLMs using the two-dimensional Political Compass Test (PCT). Initially, the PCT is employed to…