SOURCE-LINKED INTELLIGENCE

Navigating the digital spectrum: Assessing political bias, stability, and downstream fairness in Large Language Models

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Large Language Models are increasingly deployed as information intermediaries, yet measuring their political behavior remains fragile because questionnaire results mix model dispositions with measurement artifacts and response-elicitation biases. We introduce a robust Political Compass Test evaluation framework that samples 300 configurations across an eight-dimensional perturbation space varying language, framing, instructions, answer format, option order, and persona wording. We evaluate eight Gemma 3 and Qwen 3 models across 14 languages and three quantization levels, obtaining design-avera

Read original source ↗ Open in workspace

recordType

paper

region

Global