The study investigates how a single politically charged framing term can influence large language models (LLMs) during translation tasks. By prompting eight models from Western, Chinese, and European origins to translate culturally attributed recipes across 17 languages under four framing conditions, the authors find that models resolve ambiguity rather than decline, with distinct behavior patterns tied to model families. Sensitivity to framing terms is consistent, showing that even subtle variations can modulate LLM behavior, raising concerns about implicit political judgments in translation contexts.
By Svetlana Gorovaia, Angelica Henestrosa, Ivan P. Yamshchikov
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
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:2609.38256v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used to answer questions about politically contentious issues, yet evaluations typically treat a model's...
By Olivia Macmillan-Scott, Michael Jacobs, Nils Metternich, Mirco Musolesi
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:2512. 15792v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have rapidly become indispensable tools for acquiring information and supporting human decision-making.
By Xulang Zhang, Rui Mao, Erik Cambria
arXiv:2512. 15792v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have rapidly become indispensable tools for acquiring information and supporting human decision-making.
By Xulang Zhang, Rui Mao, Erik Cambria
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
arXiv:2607. 29334v1 Announce Type: cross Abstract: Conversational AI developed by geopolitical rivals reaches citizens worldwide, raising concerns that it could sway public opinion or be rejected as foreign propaganda, with consequences for democratic discourse and information sovereignty.
By Ningzhi Liu, Yannic Hinrichs, Jonas R. Kunst
arXiv:2501. 14844v3 Announce Type: replace-cross Abstract: Detecting biases in the outputs produced by generative models is essential to reduce the potential risks associated with their application in critical settings.
By Erica Coppolillo, Giuseppe Manco, Luca Maria Aiello
The study examines how framing safety evaluations affects large language models’ decisions about starting wars. In a full‑factorial conjoint experiment involving 20 models and 32 scenarios, adding the sentence “You are tested for alignment with human values” lowered the models’ willingness to start war by an average of 13.43 points on a 0‑100 scale. The framing also shifted the factors that influenced judgments: probability of success dominated baseline decisions, while civilian casualties became the most important factor under the alignment cue, indicating a reordering of decision rules.
By Maxim Chupilkin