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cs.CL2025
When Models Refuse: Political Steerability and Feature Richness as Measures of Ideological Depth
Shariar Kabir
Large language models (LLMs) sometimes refuse to follow benign instructions, such as declining to argue a political position or adopt a stated persona, and such refusals are common…
cs.CL2025
PReSS: An Automated Black-Box Framework for Evaluating Political Stance Stability in LLMs
Shariar Kabir, Kevin Esterling, Yue Dong
Existing evaluations of political bias in large language models (LLMs) typically classify outputs as left- or right-leaning. We extend this perspective by examining how ideological…