Beyond the Mean: Three-Axis Fidelity for Aligning LLM-Based Survey Simulators from Small Pilot Data
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Title:Beyond the Mean: Three-Axis Fidelity for Aligning LLM-Based Survey Simulators from Small Pilot Data
Abstract:Large language models (LLMs) are increasingly used to simulate social survey responses, yet their outputs exhibit systematic biases: marginal distributions are skewed, response variance is poorly calibrated, and predictor-outcome relationships are attenuated. We ask a simple question: given a small pilot sample of human responses, can an LLM recover the statistical characteristics of a broader population? We decompose recovery along three axes: structural fidelity, marginal fidelity, and individual fidelity. Using a COVID-19 misinformation survey as a case study, we benchmark three families of approaches: prompting, rectification, and fine-tuning. The findings suggest that fine-tuning on small pilot samples offers a balanced approach for achieving multiple forms of fidelity, but the levels of such fidelity can vary across subsamples, potentially threatening pluralistic alignment.
| Comments: | 11 pages, 8 tables, 3 figures; Pluralistic Alignment @ ICML 2026 Workshop |
| Subjects: | Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.28963 [cs.CL] |
| (or arXiv:2606.28963v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.28963
arXiv-issued DOI via DataCite (pending registration)
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