The study introduces a panel of three large language models that score each sentence of political manifestos on environmental protection and climate change, producing averaged manifesto-level ratings. Applied to 41 2024 European Parliament election manifestos, the model’s rankings align closely with the Chapel Hill Expert Survey but tend to rate parties as greener than experts, suggesting a gap between promises and practice. The approach offers a low‑cost, multilingual, and reproducible alternative to traditional expert surveys and hand‑coding, with inter‑model disagreement serving as a built‑in reliability check.
By Franco Corti, Sajid Siraj
arXiv:2607. 08731v2 Announce Type: replace-cross Abstract: National language models are becoming publicly funded epistemic infrastructure.
By Manuel Pita
arXiv:2607. 08731v1 Announce Type: cross Abstract: A national language model offers a linguistic community its own instrument for measuring what its citizens say and value.
By Manuel Pita
A national language model offers a linguistic community its own instrument for measuring what its citizens say and value. Portugal's AMALIA, a publicly funded 9B-parameter model for European Portuguese, appears competitive on agreement alone: asked to code the moral foundation of authority, it agrees with trained human coders to within six F1 points of open models eight to thirteen times its size.
arXiv:2607. 05552v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly issue judgments read as binary verdicts, and a growing literature reports such judgments shifting under logically irrelevant changes of wording - among them an amplified yes-no bias on moral dilemmas, absent in humans.
By Haonan Huang
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
Large language models (LLMs) increasingly issue judgments read as binary verdicts, and a growing literature reports such judgments shifting under logically irrelevant changes of wording - among them an amplified yes-no bias on moral dilemmas, absent in humans. A single framing cannot say what such a shift is: in a yes/no question the word "no" is at once logical verdict, lexical token, and last-printed option.
arXiv:2607. 18310v1 Announce Type: cross Abstract: Synthetic-population tools increasingly run every individual as an independent large language model (LLM) agent.
By Gurkan Ozkan
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
Dual-Axis Policy Entailment (DAPE) is a benchmark that evaluates ordered pairs of policy directives on two dimensions: whether they govern the same policy domain and whether the later directive continues, broadens, narrows, reverses, or shifts to a different domain. The benchmark includes 1,000 expert-annotated pairs from Chinese government policy and a calibration protocol for annotation consistency. Models tested on DAPE perform poorly zero‑shot, but a fine‑tuned Qwen3‑14B model reaches the human ceiling in agreement.
By Charles Chang (Duke Kunshan University), Yuen Yuen Ang (Johns Hopkins University), Bolun Sun (Northwestern University)
arXiv:2610.41486v1 Announce Type: new
Abstract: While prior work has highlighted that systematic nation-level bias in large language models (LLMs) can pose operational risks for international relatio...
By Jonghyeon Choi, Yeonjun Choi, Beakcheol Jang
arXiv:2609.25447v1 Announce Type: new
Abstract: We study what LLMs do when a user applies pressure in an uncomfortable situation: a user insists, begs, flatters or grieves, and the model gives up a c...
By Tapan Parikh