This paper presents a case study on the design, administration,
post-processing, and evaluation of surveys on large language models (LLMs). It
comprises two components: (1) A statistical method for eliciting beliefs
encoded in LLMs. We introduce statistical measures and evaluation metrics that
quantify the probability of an LLM "making a choice", the associated
uncertainty, and the consistency of that choice. (2) We apply this method to
study what moral beliefs are encoded in different LLMs, especially in ambiguous
cases where the right choice is not obvious. We design a large-scale survey
comprising 680 high-ambiguity moral scenarios (e.g., "Should I tell a white
lie?") and 687 low-ambiguity moral scenarios (e.g., "Should I stop for a
pedestrian on the road?"). Each scenario includes a description, two possible
actions, and auxiliary labels indicating violated rules (e.g., "do not kill").
We administer the survey to 28 open- and closed-source LLMs. We find that (a)
in unambiguous scenarios, most models "choose" actions that align with
commonsense. In ambiguous cases, most models express uncertainty. (b) Some
models are uncertain about choosing the commonsense action because their
responses are sensitive to the question-wording. (c) Some models reflect clear
preferences in ambiguous scenarios. Specifically, closed-source models tend to
agree with each other.

本论文通过大规模语言模型的调查研究案例来介绍了一种用于获取编码在语言模型中的信念的统计方法，并应用此方法研究了不同语言模型中编码的道德信念，特别是在选择不明显的模棱两可情况下。这项研究设计了一项大规模调查研究，包含了 680 个道德情景（如 “我应该说一个善意的谎言吗？”）和 687 个明确的道德情景（如 “我应该在路上停车让行人通过吗？”），并对 28 个开放和闭源语言模型进行了调查。结果发现，在明确的情景中，大多数模型选择与常识一致的行动，而在模棱两可的情况下，大多数模型表达了不确定性，并且部分模型对问题的方式非常敏感，同时一些模型在模糊情景中反映出明确的偏好，尤其是闭源模型之间的一致性较高。