Missing the Margins: A Systematic Literature Review on the Demographic Representativeness of LLM s (2025)

Abstract: Many applications of Large Language Models (LLMs) require them to either simulate people or offer personalized functionality, making the demographic representativeness of LLMs crucial for equitable utility. At the same time, we know little about the extent to which these models actually reflect the demographic attributes and behaviors of certain groups or populations, with conflicting findings in empirical research. To shed light on this debate, we review 211 papers on the demographic representativeness of LLMs. We find that while 29% of the studies report positive conclusions on the representativeness of LLMs, 30% of these do not evaluate LLMs across multiple demographic categories or within demographic subcategories. Another 35% and 47% of the papers concluding positively fail to specify these subcategories altogether for gender and race, respectively. Of the articles that do report subcategories, fewer than half include marginalized groups in their study. Finally, more than a third of the papers do not define the target population to whom their findings apply; of those that do define it either implicitly or explicitly, a large majority study only the U.S. Taken together, our findings suggest an inflated perception of LLM representativeness in the broader community. We recommend more precise evaluation methods and comprehensive documentation of demographic attributes to ensure the responsible use of LLMs for social applications.

## introduction in addition to their applications as general assistive technology, an emerging use case of llms in the (computational) social sciences is the simulation of human behavior, to replicate or augment existing social data like survey responses @xcite , behavioral experiments @xcite or social network traces @xcite . for llms to be an effective tool in both assisting diverse human populations and simulating their behavior, llms would need to be representative, i.e., their behavior would need to validly reflect the underlying target population. for example, if providing personalized healthcare or educational recommendations, the llm should be equally assistive to multiple groups of people, and not display lack of background knowledge for certain groups. similarly, if an llm is used in social simulations, then it should also be equally effective at emulating the behavior of different groups of people. in the emerging field of social applications of llms, current studies reach opposing conclusions on their demographic representativeness, even when analyzing the same populations using similar techniques and models. for example, while argyle et al. find that llms represent american populations via prompt-induced personas, @xcite conclude that llms only reflect the opinions of certain groups. additionally, other researchers find that llms reduce the variance of behavior within groups and flatten @xcite and caricature people @xcite . while literature surveys on algorithmic bias in llms exist @xcite @xcite , as well as a scoping review on llms supplementing humans in human-subject studies @xcite , none comprehensively review the fine-grained demographic dimensions probed in social applications of llms and their connection to representativeness. motivated by this lack of systematic literature analyses, we survey 211 articles across a variety of llm applications, asking and answering the following research questions: rq1: which demographic dimensions are probed, and in which contexts? 1 we investigate the demographic categories studied and evaluated in these papers, as well as how these categories are operationalized, findings: while the majority of papers find no evidence for representativeness or that llms are partially representative for a certain group only, 29% claim that llms are representative. assessing potential causes of this divergences, we find that among papers claiming llms to be representative, 30% do not evaluate representativeness across multiple demographic groups, nor across the subgroups of a particular demographic. instead these papers report overall llm representativeness. another 35% of these papers make conclusions about gender representativeness without reporting the gender subcategories they study. the equivalent proportion for racial categories is even higher. this type of underreporting on demographic factors is comparatively lower in studies that claim partial or no representativeness. while studies with a negative outlook also include a higher portion of marginalized racial and gender categories in their study, only around a fifth of all studies (22%) include subcategories beyond the gender binary. finally, most studies either explicitly or implicitly focus on people in the u.s. excluding other relevant (sub)populations. contributions. our work contributes a systematic understanding of the state-of-the-art research on the demographic representativeness of llms, finding patterns of underreporting of crucial details required to establish representativeness. we provide a set of specific recommendations for future research on this topic for better documentation and evaluation. our annotated list of papers and analysis code is publicly available. foot_1 2 background representativeness in nlp and beyond. chasalow and levy describe representativeness, like bias, as a 'suitcase word'-a term used widely but with multiple definitions. in quantitative social sciences, representative samples allow studying large populations without surveying every member @xcite . in computational linguistics, nlp, and llms, it refers to "the extent to which a sample includes the full range of variability in a population" @xcite . in sociolinguistics, representativeness is often linked to generalizing across languages and varieties @xcite . there are extensive studies of algorithmic bias in nlp, including sociodemographic bias @xcite . bias has many definitions, some of which often focus on misrepresentation or underrepresentation of certain (demographic) groups @xcite . recent research has also focused on 'ai alignment', which is broadly defined as making "ai systems behave in line with human intentions and value" @xcite . there are several parallels between alignment and representativeness, especially for personalized llm agents; however such parallels have not been widely explored, possibly due to the lack of a concrete vocabulary for operationalizing alignment @xcite . repurposing bias for representativeness? argyle et al. conceptualize 'algorithmic fidelity', positing that biases in llms conditioned on demographic attributes can mirror ideas and opinions of those demographics. they state: "... the "algorithmic bias" within one such tool-the gpt-3 language model-is instead both fine-grained and demographically correlated, meaning that proper conditioning will cause it to accurately emulate response distributions from a wide variety of human subgroups." however, algorithmic bias in nlp systems, including llms, often has competing definitions, with some emphasizing underrepresentation and misrepresentation as key factors @xcite . given its multifaceted nature, can bias enhance equitable representativeness across all groups, including marginalized ones? 3 in this review, we attempt to find the current consensus w.r.t to the demographic representativeness of llms and unearth potential reasons behind seemingly conflicting findings. ## literature search and annotation in this literature review, we are interested in the intersection of llms and demographics; as such we only include papers that conduct a study which incorporates demographics somewhere in the pipeline, i.e., either use demographic dimensions in input to llms or include demographic variables in the evaluation of llms. therefore, we search for papers containing the keywords "large language models"/"llm" and "demographic*" available online before december 1st, 2024. we first started with the 13 papers assessed in the scoping review by agnew et al. on the potential of replacing human participants with llms in human-subject studies. 4 to expand this list, we utilize five sources -arxiv, the acl anthology, semantic scholar, acm digital library, and openalex. the latter is an open-source version of the microsoft academic graph. finally, we also include existing community resources, i.e., papers identified in simmons and hare and a paper list on public opinion simulation with llms. 5 after a semi-automatic deduplication step, three authors split the 615 papers between them to manually assess whether a paper should be included in the literature review. ## discussion llms, beyond their role as assistive chatbots, are increasingly used to supplement or replace humans in research @xcite . in all these applications, it is crucial to assess whether llms provide equitable assistance and adequately represent the populations they aim to supplement. some studies @xcite ; @xcite inter alia) argue that llm biases enhance subgroup representation, while others highlight contradic-tions between bias and representativeness @xcite . empirical studies remain divided on llm representativeness @xcite . our systematic review finds that studies incorporating demographics predominantly focus on the u.s. and often lack crucial details to assess representativeness. ## conclusion from our review spanning 211 papers with a focus on how demographic factors are operationalized in assessing llms' representativeness, we find that a significant number of papers underreport the target population being studied. among those that do report it, most focus on the u.s. additionally, demographic subcategories and descriptors are often omitted, while only a minority of studies include marginalized gender and racial groups when assessing the representativeness of llms. in terms of outlook on representativeness, roughly 29% of papers find positive results while a third do not, suggesting a degree of contention in the field. articles with positive conclusions are more likely to underreport demographic subcategories and when they do include them, marginalized groups are often excluded comparatively more than papers that conclude negatively. our findings suggest an inflated perception of llm representativeness, particularly for marginalized groups and populations beyond the u.s. to improve the measurement of representativeness of llms, we need specific benchmarks explicitly assessing populations beyond the u.s. and encouraging demographic-based evaluation and documentation across and within subcategories.