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arXiv Computation and Language By Daniel C. Ruiz, Anna Serbina, Ashwin Rao, Emilio Ferrara, Luca Luceri

How Far Will They Go? Red-Teaming Online Influence with Large Language Models

Read the original on arXiv Computation and Language →

The paper presents a red‑teaming framework to evaluate how open‑source large language models (LLMs) can be used for political influence, focusing on models that can be deployed locally without weight modifications. It measures LLM Overton Windows (OWs) to assess expression fidelity of sampled political opinions and examines how natural‑language jailbreaks alter these scores. The study evaluates over 30 LLMs from 10 families across five countries, finding that left‑leaning content is expressed with higher fidelity, larger models tend to have lower mean OW scores, and jailbreak effectiveness varies significantly between model families.

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.

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