Uncovering latent values and opinions embedded in large language models (LLMs) can help identify biases and mitigate potential harm. Recently, this has been approached by prompting LLMs with survey questions and quantifying the stances in the outputs 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 natural 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.

翻译：揭示大型语言模型（LLMs）中隐含的价值观与观点有助于识别偏见并减轻潜在危害。近期研究通过向LLMs输入调查问题，并量化其输出中对道德与政治敏感声明的立场来实现这一目标。然而，LLMs生成的立场可能因提示方式不同而产生显著差异，且支持或反对特定立场存在多种论证方式。本研究通过分析一个大规模鲁棒数据集来解决该问题：该数据集包含6个LLM使用420种提示变体对政治指南针测试（PCT）62个命题生成的15.6万条响应。我们对其生成立场进行粗粒度分析，并对立场背后的纯文本论证进行细粒度分析。在细粒度分析中，我们提出识别响应中的"惯用表达"：即在不同提示下反复出现且保持一致的语义相似短语，这些短语揭示了特定LLM倾向于生成的文本自然模式。研究发现，提示中添加的人口统计学特征会显著影响PCT测试结果，这既反映了模型偏见，也揭示了封闭形式响应与开放域响应测试结果之间的差异。此外，通过惯用表达对纯文本论证模式的分析表明，即使立场迥异，相似论证理由仍会在不同模型和提示中反复生成。