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: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: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: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:2607. 27232v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly shaping how we consume information and form our worldview.
By Haran Shani-Narkiss, Michael Fire, Oren Tsur
arXiv:2607. 09262v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to summarize and evaluate policy-relevant information, but it remains unclear whether their judgments are implicitly shaped by geopolitical cues.
By Maxim Chupilkin
The study examines how language influences AI chatbot responses to questions about the war in Ukraine, revealing that the same AI systems (GPT, Claude, Gemini) produce varying political stances across 112 languages. By evaluating 20 statements in 112 languages, the researchers found that Russia‑leaning versus Ukraine‑leaning answers differ by language, mirroring global political attitudes such as public support for Russia, UN voting patterns, and aid levels. This pattern persists across all three models and even when specific statement pairs are removed, suggesting that information warfare could embed geopolitical biases into AI training data.
By Maxim Chupilkin
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: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: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: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