  Uncovering latent values and opinions in large language models (LLMs) can
help identify biases and mitigate potential harm. Recently, this has been
approached by presenting LLMs with survey questions and quantifying their
stances towards morally and politically charged statements. However, the
stances generated by LLMs can vary greatly depending on how they are prompted,
and there are many ways to argue for or against a given position. In this work,
we propose to address this by analysing a large and robust dataset of 156k LLM
responses to the 62 propositions of the Political Compass Test (PCT) generated
by 6 LLMs using 420 prompt variations. We perform coarse-grained analysis of
their generated stances and fine-grained analysis of the plain text
justifications for those stances. For fine-grained analysis, we propose to
identify tropes in the responses: semantically similar phrases that are
recurrent and consistent across different prompts, revealing patterns in the
text that a given LLM is prone to produce. We find that demographic features
added to prompts significantly affect outcomes on the PCT, reflecting bias, as
well as disparities between the results of tests when eliciting closed-form vs.
open domain responses. Additionally, patterns in the plain text rationales via
tropes show that similar justifications are repeatedly generated across models
and prompts even with disparate stances.
