As large language models (LLMs) become more capable, there is growing excitement about the possibility of using LLMs as proxies for humans in real-world tasks where subjective labels are desired, such as in surveys and opinion polling. One widely-cited barrier to the adoption of LLMs is their sensitivity to prompt wording - but interestingly, humans also display sensitivities to instruction changes in the form of response biases. As such, we argue that if LLMs are going to be used to approximate human opinions, it is necessary to investigate the extent to which LLMs also reflect human response biases, if at all. In this work, we use survey design as a case study, where human response biases caused by permutations in wordings of "prompts" have been extensively studied. Drawing from prior work in social psychology, we design a dataset and propose a framework to evaluate whether LLMs exhibit human-like response biases in survey questionnaires. Our comprehensive evaluation of nine models shows that popular open and commercial LLMs generally fail to reflect human-like behavior. These inconsistencies tend to be more prominent in models that have been instruction fine-tuned. Furthermore, even if a model shows a significant change in the same direction as humans, we find that perturbations that are not meant to elicit significant changes in humans may also result in a similar change. These results highlight the potential pitfalls of using LLMs to substitute humans in parts of the annotation pipeline, and further underscore the importance of finer-grained characterizations of model behavior. Our code, dataset, and collected samples are available at https://github.com/lindiatjuatja/BiasMonkey

翻译：随着大语言模型（LLMs）能力的增强，人们日益期待将其作为人类代理来执行需要主观标签的真实世界任务，例如调查和民意测验。一个被广泛引用的阻碍LLMs应用的因素是其对提示措辞的敏感性——但有趣的是，人类在面对指令变化时也会表现出响应偏差。因此，我们认为，如果要将LLMs用于近似人类意见，必须研究LLMs在何种程度上（如果有的话）也反映了人类的响应偏差。本工作以调查设计为案例研究，其中因“提示”措辞变化引起的人类响应偏差已有广泛研究。借鉴社会心理学的先前研究，我们设计了一个数据集并提出了一个评估框架，用于检验LLMs在调查问卷中是否表现出类似人类的响应偏差。我们对九个模型的全面评估显示，流行的开源和商业LLMs通常未能反映类似人类的行为。这些不一致性在经过指令微调的模型中往往更为显著。此外，即使某个模型表现出与人类相同方向的显著变化，我们发现那些旨在不引起人类显著变化的扰动也可能导致类似变化。这些结果凸显了用LLMs替代人类完成标注流程部分工作的潜在陷阱，并进一步强调了更细致刻画模型行为的重要性。我们的代码、数据集和收集的样本已公开于 https://github.com/lindiatjuatja/BiasMonkey