arXiv AI June 8, 2026 By Upasana Chatterjee

Does Topic Sentiment Cause Perceived Ideology? Comparing Human and LLM Annotations in Political News Articles

arXiv:2606. 06715v1 Announce Type: cross Abstract: We ask whether topic sentiment has a causal effect on perceived political ideology, and whether the answer depends on who assigns the ideology label.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 17

Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs

arXiv:2607. 14888v1 Announce Type: cross Abstract: Finetuning language models on small, curated datasets is standard practice for adapting them to specific policies or domains.

By Robert Graham, Edward Stevinson, Yariv Barsheshat

llms fine-tuning benchmarks safety

arXiv AI

Jul 14

Ideological Bias in LLMs' Economic Causal Reasoning

arXiv:2604. 21334v2 Announce Type: replace Abstract: Do large language models (LLMs) exhibit systematic ideological bias when reasoning about economic causal effects?

By Donggyu Lee, Hyeok Yun, Jungwon Kim, Junsik Min, Sungwon Park, Sangyoon Park, Jihee Kim

llms benchmarks safety

arXiv Computation and Language

Aug 25

Definitional Sensitivity in Media Bias Detection: A Multi-Definition Dataset and Benchmark

arXiv:2608.23095v1 Announce Type: new Abstract: Media bias detection relies on definitions and examples that specify what counts as bias, yet these specifications often vary across datasets or remain...

By Martin Wessel, Timo Spinde, J\"urgen Pfeffer, Gianluca Demartini

llms benchmarks safety

arXiv Computation and Language

Sep 22

What Is The Political Content in LLMs' Pre- and Post-Training Data?

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

llms

arXiv Computation and Language

Sep 10

A Resource for Enthymeme Detection in Controversial Political Discourse

arXiv:2606.12186v2 Announce Type: replace Abstract: Enthymemes, arguments with unstated premises or conclusions, are pervasive in persuasive discourse, yet their annotation remains notoriously subjec...

By Martial Pastor, Nelleke Oostdijk

nlp

arXiv AI

Sep 1

How Identity and Opinion Shape Political Sycophancy in LLMs

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

llms benchmarks safety

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Related stories

Innocuous-Seeming Data, Latent Ideology: Ideological Generalisation in Finetuned LLMs

Ideological Bias in LLMs' Economic Causal Reasoning

Definitional Sensitivity in Media Bias Detection: A Multi-Definition Dataset and Benchmark

What Is The Political Content in LLMs' Pre- and Post-Training Data?

A Resource for Enthymeme Detection in Controversial Political Discourse

How Identity and Opinion Shape Political Sycophancy in LLMs