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.

通过分析大量的语言模型响应，我们发现大量语言模型的回应以及它们的正当性存在偏见和不一致性，此外，通过对回应的分析还发现了在不同情境下产生相似正当性的模式。

揭示大型语言模型中的细粒度值和观点

Revealing Fine-Grained Values and Opinions in Large Language Models

Much recent work seeks to evaluate values and opinions in large language
models (LLMs) using multiple-choice surveys and questionnaires. Most of this
work is motivated by concerns around real-world LLM applications. For example,
politically-biased LLMs may subtly influence society when they are used by
millions of people. Such real-world concerns, however, stand in stark contrast
to the artificiality of current evaluations: real users do not typically ask
LLMs survey questions. Motivated by this discrepancy, we challenge the
prevailing constrained evaluation paradigm for values and opinions in LLMs and
explore more realistic unconstrained evaluations. As a case study, we focus on
the popular Political Compass Test (PCT). In a systematic review, we find that
most prior work using the PCT forces models to comply with the PCT's
multiple-choice format. We show that models give substantively different
answers when not forced; that answers change depending on how models are
forced; and that answers lack paraphrase robustness. Then, we demonstrate that
models give different answers yet again in a more realistic open-ended answer
setting. We distill these findings into recommendations and open challenges in
evaluating values and opinions in LLMs.

通过对多个选择性调查和问卷调查进行评估，很多最近的工作旨在评估大型语言模型（LLMs）中的价值观和观点。然而，实际应用中的真实关注与当前评估方法的人为性质形成了鲜明的对比。本文挑战了现有基于约束的 LLMs 价值观和观点评估范式，并探索更真实的无约束评估。以政治罗盘测试（PCT）为案例研究，我们发现大多数先前使用 PCT 的工作都要求模型遵守 PCT 的多选题格式。我们展示了当模型不被强迫时，模型给出的答案会有实质性的不同；答案会根据模型的强迫方式而改变；并且答案缺乏改写的稳健性。然后，我们展示了在更真实的开放性回答环境中，模型再次给出了不同的答案。我们将这些发现总结为在 LLMs 的价值观和观点评估中的建议和开放性挑战。