The versatility of Large Language Models (LLMs) on natural language understanding tasks has made them popular for research in social sciences. In particular, to properly understand the properties and innate personas of LLMs, researchers have performed studies that involve using prompts in the form of questions that ask LLMs of particular opinions. In this study, we take a cautionary step back and examine whether the current format of prompting enables LLMs to provide responses in a consistent and robust manner. We first construct a dataset that contains 693 questions encompassing 39 different instruments of persona measurement on 115 persona axes. Additionally, we design a set of prompts containing minor variations and examine LLM's capabilities to generate accurate answers, as well as consistency variations to examine their consistency towards simple perturbations such as switching the option order. Our experiments on 15 different open-source LLMs reveal that even simple perturbations are sufficient to significantly downgrade a model's question-answering ability, and that most LLMs have low negation consistency. Our results suggest that the currently widespread practice of prompting is insufficient to accurately capture model perceptions, and we discuss potential alternatives to improve such issues.

翻译：大型语言模型（LLM）在自然语言理解任务上的广泛适用性使其成为社会科学研究的热门工具。为深入理解LLM的特性与固有行为模式，研究者们通过设计包含特定观点询问的提示问题开展实验。本研究对此持审慎态度，系统考察当前提示格式是否能使LLM保持稳定一致的响应能力。我们首先构建了一个包含693个问题的数据集，覆盖了115个人格轴上的39种不同人格测量工具。同时，我们设计了一组包含微小变化的提示，测试LLM生成准确答案的能力，并通过一致性变体实验检验其对简单扰动（如选项顺序调换）的响应稳定性。在15个开源LLM上的实验表明：即使是简单的扰动也足以显著降低模型的问答能力，且多数LLM对否定表述的一致性欠佳。研究结果表明，当前广泛采用的提示方法不足以准确捕捉模型认知，我们据此讨论了改进此类问题的潜在替代方案。