arXiv Computation and Language November 4, 2026 By Franco Corti, Sajid Siraj

How Green Is Your Manifesto? Scoring Environmental Positions in the 2024 EU Elections with an LLM Panel

Read the original on arXiv Computation and Language →

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.

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 Computation and Language.

arXiv AI

6d ago

The Model as One Rater Among Several: Measuring Political Positions in Data-Sparse Regions with a Language-Model Panel

The paper introduces a method for measuring political positions in data‑sparse regions by treating a large language model as a single fallible rater within a panel, analogous to expert surveys. It demonstrates that this panel approach yields reliable agreement across multiple models and reveals that disagreements often stem from differing interpretations of the regime rather than factual errors. The authors provide a full rubric, prompt, and data set, using the Middle East and North Africa as a worked example.

By Tarek Gara

llms safety

arXiv AI

Jul 17

Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents

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

llms rag agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Sep 15

Issue Bias in Generative AI Writing Assistance: Political Issues and LLMs in the Swedish 2026 Election

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

llms safety

Hugging Face Trending Papers

Jul 9

Validity of LLMs as data annotators: AMALIA on authority

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.

llms benchmarks

arXiv AI

Jul 10

Validity of LLMs as data annotators: AMALIA on authority

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

llms benchmarks

arXiv AI

Jul 21

Trusting sovereign language models as scientific instruments: evidence from Portugal's AMALIA

arXiv:2607. 08731v2 Announce Type: replace-cross Abstract: National language models are becoming publicly funded epistemic infrastructure.

By Manuel Pita

llms

One email a morning, machine-written

Related stories

The Model as One Rater Among Several: Measuring Political Positions in Data-Sparse Regions with a Language-Model Panel

Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents

Issue Bias in Generative AI Writing Assistance: Political Issues and LLMs in the Swedish 2026 Election

Validity of LLMs as data annotators: AMALIA on authority

Validity of LLMs as data annotators: AMALIA on authority

Trusting sovereign language models as scientific instruments: evidence from Portugal's AMALIA