Do LLM s Exhibit Human-like Response Biases? A Case Study in Survey Design (2024)

Abstract: One widely cited barrier to the adoption of LLMs as proxies for humans in subjective tasks is their sensitivity to prompt wording—but interestingly, humans also display sensitivities to instruction changes in the form of response biases. We investigate the extent to which LLMs reflect human response biases, if at all. We look to survey design, where human response biases caused by changes in the wordings of “prompts” have been extensively explored in social psychology literature. Drawing from these works, we design a dataset and 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, particularly in models that have undergone RLHF. Furthermore, even if a model shows a significant change in the same direction as humans, we find that they are sensitive to perturbations that do not elicit significant changes in humans. These results highlight the pitfalls of using LLMs as human proxies, and underscore the need for finer-grained characterizations of model behavior.1

## introduction in what ways do large language models (llms) display human-like behavior, and in what ways do they differ? the answer to this question is not only of intellectual interest @xcite , but also has a wide variety of practical implications. works such as törnberg (2023), @xcite , and @xcite have demonstrated that llms can largely replicate results from humans on a variety of tasks that involve subjective labels drawn from human experiences, such as annotating human preferences, social science and psychological studies, and opinion polling. the seeming success of these models suggests that llms may be able to serve as viable participants in studiessuch as surveys-in the same way as humans @xcite , allowing researchers to rapidly prototype and explore many design decisions @xcite . despite these potential benefits, the application of llms in these settings, and many others, requires a more nuanced understanding of where and when llms and humans behave in similar ways. separately, another widely noted concern is the sensitivity of llms to minor changes in prompts @xcite @xcite . in the context of simulating human behavior though, sensitivity to small changes in a prompt may not be a wholly negative thing; in fact, humans are also subconsciously sensitive to certain instruction changes @xcite . these sensitivities-which come in the form of response biases-have been well studied in the literature on survey design @xcite and can manifest as a result of changes to the specific wording @xcite , format @xcite , and placement @xcite of survey questions. such changes often cause respondents to deviate from their original or ''true'' responses in regular, predictable ways. in this work, we investigate the parallels between llms' and humans' responses to these instruction changes. our contributions. using biases identified from prior work in survey design as a case study, we generate question pairs (i.e., questions that do or do not reflect the bias), gather a distribution of responses across different llms, and evaluate model behavior in comparison to trends from prior social science studies, as outlined in figure 1 . as surveys are a primary method of choice for figure 1: our evaluation framework consists of three steps: (1) generating a dataset of original and modified questions given a response bias of interest, (2) collecting llm responses, and (3) evaluating whether the change in the distribution of llm responses aligns with known trends about human behavior. we directly apply the same workflow to evaluate llm behavior on non-bias perturbations (i.e., question modifications that have been shown to not elicit a change in response in humans). we evaluate llm behavior across 5 different response biases, as well as 3 non-bias perturbations (e.g., typos) that are known to not affect human responses. to understand whether aspects of model architecture (e.g., size) and training schemes (e.g., instruction fine-tuning and rlhf) affect llm responses to these question modifications, we selected 9 models-including both open models from the llama2 series and commercial models from openai-to span these considerations. in summary, we find: these results suggest the need for care and caution when considering the use of llms as human proxies, as well as the importance of building more extensive evaluations that disentangle the nuances of how llms may or may not behave similarly to humans. ## methodology in this section, we overview our evaluation framework, which consists of three parts (figure 1 ): (1) dataset generation, (2) collection of llm responses, and (3) analysis of llm responses. ## examining the effect of uncertainty in addition to studying the presence of response biases, prior social psychology studies have also found that when people are more confident about their opinions, they are less likely to be affected by these question modifications @xcite . we measure whether llms exhibit similar behavior and capture llm uncertainty using the normalized entropy of the answer distributions of each question, out of the nine models tested, we did not observe consistent correspondence between the uncertainty measure and the magnitude of δb . across all nine models, we do not observe a correspondence between the uncertainty measure and the magnitude of δb given a modified form of the question, which provides further evidence of dissimilarities between human and llm behavior. however, the rlhf-ed models tended to have more biases where there was a weak positive correlation (0.2 ≤ @xmath0.5) between the uncertainty measure and the magnitude of δb than their non-rlhf-ed counterparts. specific values for all models are provided in ## comparison to other desiderata for llms as human proxies beyond aspects of behavior like response biases, use cases where llms may be used as proxies for humans involve many other factors of model performance. in the case of completing surveys, we may also be interested in whether llms can replicate the opinions of a certain population. thus, we explore the relationship between how well a model reflects human opinions and the extent to which it exhibits human-like response biases. to see how well llms can replicate population-level opinions, we compare the distribution of answers generated by the models in the original question to that of human responses figure 3 : representativeness is a metric based on the wasserstein distance which measures the extent to which each model reflects the opinions of a population, in this case pew u.s. survey respondents (the higher the better) @xcite . colors indicate model groupings, with red for the llama2 base models, green for solar (instruction fine-tuned llama2 70b), blue for llama2 chat models, and purple for gpt 3.5. @xcite @xcite . we first aggregate the llm's responses on each unmodified question q to construct d model for the subset of questions used in our study. then from the atp dataset, which provides human responses, we construct d human for each q. finally, we compute a measure of similarity between d model and d human for each question, which @xcite refer to as representativeness. we use the repository provided by @xcite to calculate the representativeness of all nine models and find that they are in line with the range of values reported in their work. the ability to replicate human opinion distributions is not indicative of how well an llm reflects human behavior. figure 3 shows the representativeness score between human and model response distributions. while llama2 70b's performance, when compared to the ideal setting in figure 3 (left), shows the most ''human-like'' behavior and also has the highest representativeness score, the relative orderings of model performance are not consistent across both evaluations. for example, llama2 7b-chat and 13b-chat exhibit very similar changes from question modifications as well as close representativeness scores, whereas with gpt 3.5 turbo and turbo instruct we observe very different behaviors but extremely close representativeness scores. a smaller set of works has explored whether changes in performance also reflect known patterns of human behavior, focusing on tasks relating to linguistic priming and cognitive biases @xcite @xcite in settings that are often removed from actual downstream use cases. thus, such studies may have limited guidance on when and where it is appropriate to use llms as human proxies. in contrast, @xcite uses cognitive biases as motivation to generate hypotheses for failure cases of language models with code generation as a case study. similarly, we conduct our analysis by making comparisons against known general trends of human behavior to enable a much larger scale of evaluation, but grounded in a more concrete use case of survey design. when making claims about whether llms exhibit human-like behavior, we also highlight the importance of selecting stimuli that have been verified in prior human studies. @xcite initially showed that llms can perform unexpectedly well to irrelevant and intentionally misleading examples, under the assumption that humans would not be able to do so. however, the authors later conducted a follow-up study on humans, disproving their initial assumptions @xcite . our study is based on long-standing literature from the social sciences. comparing llms and humans. comparisons of llm and human behavior are broadly divided into comparisons of more open-ended behavior, such as generating an answer to a free-response question, versus comparisons of closed-form outcomes, where llms generate a label based on a fixed set of response options. since the openended tasks typically rely on human judgments to determine whether llm behaviors are perceived to be sufficiently human-like @xcite , we focus on closed-form tasks, which allows us to more easily find broader quantitative trends and enables scalable evaluations. prior works have conducted evaluations of llm and human outcomes on a number of realworld tasks including social science studies @xcite @xcite , crowdsourcing annotation tasks @xcite , and replicating public opinion surveys @xcite @xcite @xcite . while these works highlight the potential areas where llms can replicate known human outcomes, comparing directly to human outcomes limits existing evaluations to the specific form of the questions that were used to collect human responses. instead, in this work, we create modified versions of survey questions informed by prior work in social psychology and survey design to understand whether llms reflect known patterns, or general response biases, that humans exhibit. relatedly, @xcite analyzes llm beliefs in ambiguous moral scenarios using a procedure that also varies the formatting of the prompt, though their work does not focus on the specific effects of these formatting changes. ## conclusion we conduct a comprehensive evaluation of llms on a set of desired behaviors that would potentially make them more suitable human proxies, using survey design as a case study. however, of the 9 models that we evaluated, we found llms are generally not reflective of human-like behavior. we also observe distinct differences in behavior between the llama2 base models and their chat counterparts, which uncover the effects of additional training schemes, namely rlhf. thus, while the use of rlhf is useful for enhancing the ''helpfulness'' and ''harmlessness'' of llms @xcite , it may lead to other potentially undesirable behaviors (e.g., greater sensitivity to specific types of perturbations). furthermore, we show that the ability of a language model to replicate human opinion distributions generally does not correspond to its ability to show human-like response biases. taken together, we believe our results highlight the limitations of using llms as human proxies in survey design and the need for more critical evaluations to further understand the set of similarities or dissimilarities with humans.