When Can You Trust Your Synthetic Users? Diagnostics and Corrections for LLM Consumer Panels

Large language models (LLMs) are increasingly used as synthetic users, stand-ins for human respondents whose simulated answers feed product, policy, and market decisions. We ask when this substitution is valid and when it fails, and package the answer as an evaluation framework for intelligent synthetic-user systems. A single protocol, run across four models spanning two families and an 8B-to-frontier capability range, is applied to two independent domains of real human-response data: U.S. general social attitudes (General Social Survey) and cross-cultural values (World Values Survey). Every model is benchmarked against a suite of non-LLM baselines fit on held-out human data. Under demographic prompting and the survey-simulation protocols we test, two failures replicate across both domains, all four models, and both families. First, at the individual level no LLM beats even the strongest baseline; on cross-cultural values every model falls well below it, and the gap survives distance-aware and proper scoring. Second, models systematically over-determine demographics, treating identity as far more predictive of attitudes than it is among real people, a distortion present for nearly every question-group combination and robust to a coding-invariant measure. Neither failure is remedied by a larger, more capable model. A decision-impact analysis shows why this matters in practice: on a segment-targeting task the models inflate between-segment gaps two to fourfold, would direct a team to the wrong segment in half of U.S. and most cross-cultural cases, and manufacture segment splits that do not exist in real people. We publicly release the cross-domain benchmark and validation framework, including all code and data, so that teams can determine in advance when synthetic-user evidence is safe for decision support and when it is not.

Large language models (LLMs) are increasingly used to generate synthetic survey respondents and digital twins of real people, but whether their output preserves real human statistical structure, rather than surface plausibility, remains unresolved, and most existing evidence comes from proprietary models rather than open-weight ones. We evaluate three open-weight LLM families on a cross-instrument calibration task: conditioning personas on real respondents' verbatim answers to one psychometric instrument and measuring them on a second, construct-distance-controlled instrument, checked against a 2,058-person human panel. Across a 139-pair grid, the simulated cross-instrument correlation tracks the real human correlation at r = 0.70 - 0.73 in every model, driven mainly by correct sign rather than precise magnitude and concentrated in pairs of moderate construct distance. A correlation of this magnitude, obtained from untuned open-weight models conditioned only on individual-level survey data, is a substantively encouraging result for LLM-based behavioral simulation and digital-twin applications: specific model families and releases already reproduce a meaningful share of real human cross-instrument structure without any fine-tuning. This capability does not, however, improve monotonically across model releases: on a matched panel, the newest of three tested Llama releases performs worst on two of three headline metrics, so realizing its promise in practice requires release-specific, distance-aware verification rather than a one-time benchmark.

Large language models (LLMs) are increasingly used as synthetic survey respondents, but existing evaluations mostly ask whether individual answers look plausible. We ask a stricter, psychometric question: do LLMs preserve the joint distribution, latent structure, reliability, mediation pathways, and demographic effects of real human survey data? We introduce an open Lithuanian organisational-psychology dataset (n=263 employees; three validated instruments; 68 items, 12 subscales) and condition 37 LLMs on real respondent profiles under a persona-disclosure ladder, presentation, reasoning-effort and cross-language ablations, counterfactual demographic swaps, and a memorization probe. The resulting Psychometric Similarity Score (PSS) is anchored against five statistical baselines, a held-out human ceiling, and the real answers of the same respondents. LLMs reproduce the direction of human psychometric relationships, but statistical baselines with no language model match or beat them. The best LLM (PSS 0.81) is level with a Gaussian copula fit only on other respondents (0.82) and below the real answers of the same 100 people (0.89). A simple classifier separates every LLM sample from humans (median AUC 0.99), and verbatim recall of the items is rare (at most 4.7%) and unrelated to PSS. Counterfactual swaps move answers in the direction of human group differences and by a similar amount (role 0.49 and education 0.33 human SD, vs. 0.46 and 0.44 in humans). Gender swaps move them by 0.06 SD. Tucker's phi on UWES falls inside an item-permutation null for 10 of 37 models. Synthetic respondents compress the response scale (within-respondent SD 0.79 vs. 1.19), lose predictive validity on held-out humans (mean R^2=-0.14 vs. 0.28), and produce significant indirect effects on 4 of 7 mediation paths that are null in humans. LLM samples are therefore not a drop-in replacement for human survey data.