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, suggesting that such a result could be
partially due to other spurious correlations. 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 this https URL

大型语言模型在代表人类进行主观标签任务方面的潜力和限制