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.
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...
By Bastiaan Bruinsma, Annika Fred\'en, Paul R\"ottger, Moa Johansson, Asad Sayeed
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:2606. 28335v1 Announce Type: cross Abstract: We argue, with systematic empirical evidence, that a large language model's political ideology is not a fixed point, but a conditional distribution $\mathbb{P}($position$\mid$context$)$ over a real political space.
By Adib Sakhawat, Syed Rifat Raiyan, Tahsin Islam, Takia Farhin, Hasan Mahmud, Md Kamrul Hasan
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: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: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
The paper evaluates large language models (LLMs) for annotating German parliamentary debates on migration, achieving macro‑F1 scores comparable to human agreement, particularly with GPT‑5 and gpt‑oss‑120B. It combines soft‑label outputs with Design‑based Supervised Learning to mitigate systematic bias and applies the method to a 150‑plus‑year corpus, revealing high solidarity post‑war and a sharp rise in anti‑solidarity since 2015. The study demonstrates that LLMs can enable large‑scale social‑scientific analysis while highlighting the need for rigorous validation and bias correction.
By Aida Kostikova, Ole P\"utz, Steffen Eger, Olga Sabelfeld, Benjamin Paassen
arXiv:2509.22367v3 Announce Type: replace
Abstract: Large language models (LLMs) reflect politically-slanted opinions in their generated text. Even though it is widely assumed that model behavior ste...
By Tanise Ceron, Dmitry Nikolaev, Dominik Stammbach, Debora Nozza
arXiv:2505. 24539v4 Announce Type: replace-cross Abstract: We present a study on how and where personas -- defined by distinct sets of human characteristics, values, and beliefs -- are encoded in the representation space of large language models (LLMs).
By Celia Cintas, Miriam Rateike, Erik Miehling, Elizabeth Daly, Skyler Speakman
arXiv:2510.15125v3 Announce Type: replace-cross
Abstract: Social media platforms play a pivotal role in shaping political discourse, but the scale and rapid evolution of online content make systemati...
By Alexander Brady, Tunazzina Islam
PolERo presents a new dataset of 3,574 Romanian question‑answer pairs from presidential transcripts, annotated for political evasion using a two‑level taxonomy of response clarity and fine‑grained evasion strategies. The study evaluates various classification methods—including TF‑IDF baselines, fine‑tuned encoders, a sliding‑window encoder, and zero/few‑shot LLM prompting—under matched conditions. Cross‑lingual transfer experiments via joint bilingual training and machine‑translation augmentation reveal that fine‑tuned encoders perform competitively, transfer is asymmetric, and ambivalent evasion categories with pragmatic cues remain the most challenging across all models.
By Gabriel Stefan, Sergiu Nisioi