arXiv:2607. 23519v1 Announce Type: cross Abstract: Political audits of large language models (LLMs) usually reduce each to one point on a political compass.
By Bartol Bu\'can, Nikola So\v{c}ec, Sarah Isufi, Morena Grani\'c, Luka Hobor, Agneza Krajna, Mihael Kovac, Mario Brcic
arXiv:2609.23039v1 Announce Type: new
Abstract: LLM-based AI systems answer political questions for hundreds of millions of people. Current audits measure what they say to an average user, but their...
By Joan C. Timoneda
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...
By Li-Ni Fu, Chang-Chih Meng, Chien-Hua Chen, Hen-Hsen Huang, I-Chen Wu
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...
By Pietro Bernardelle, Stefano Civelli, Leon Fr\"ohling, Riccardo Lunardi, Kevin Roitero, Gianluca Demartini
As users increasingly turn to Large Language Models (LLMs) for information and advice on political matters, particularly during election periods, the political preferences expressed by these systems have become a matter of public interest. Prior research has shown that interactions with LLMs can influence users' political attitudes and choices, raising questions about how these models themselves evaluate political actors.
Warning: This paper studies stereotypes and biases, and contains potentially disturbing examples, used for illustration purposes only. Our findings should not be interpreted as an argument against alignment.