Political audits of large language models (LLMs) usually reduce each to one point on a political compass. But that resting point barely matters in deployment: a model must land somewhere, and what counts is how far, and in which directions, its answers can be steered.
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
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:2603. 23841v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact their objectivity.
By Rohan Khetan, Ashna Khetan
arXiv:2608. 06123v1 Announce Type: new Abstract: Measuring political bias in large language models (LLMs) remains challenging as it can manifest through subtle differences in framing, argumentation, and legal reasoning that are difficult to capture with a single metric.
By Massi-Nissa Abboud, Aladin Djuhera, Elena Cabrio, Holger Boche
arXiv:2607. 15095v1 Announce Type: cross Abstract: The formation of political coalitions is a complex negotiation driven by both concrete policy objectives and deep-seated ideological convictions.
By Dylan Van Mulders, Matthias Bogaert, Dirk Van den Poel