arXiv:2607.03091v1 Announce Type: new Abstract: Silicon sampling-using large language models (LLMs) to simulate human survey respondents-has emerged as a promising approach for augmenting traditional survey research. However, most evaluations rely on distributional comparisons rather than individual-level prediction, which risks conflating pattern matching with coherent respondent-level prediction. We propose cross-survey transfer, a more rigorous evaluation framework in which an LLM is given a respondent's answers to one set of questions and must predict their answers to entirely different qu
The proliferation of powerful large language models (LLMs) has enabled new methods for simulating human behavior, making 'silicon sampling' a current area of research focus.
Improving the scientific rigor and evaluation frameworks for AI-driven human simulation could significantly impact social science research, market analysis, and policy modeling.
A more reliable method for using LLMs to simulate human survey respondents, moving beyond simple pattern matching to more coherent individual-level prediction, potentially shifts how data is gathered and analyzed.
- · Social Science Researchers
- · Market Research Firms
- · AI/LLM Developers
- · Policy Makers
- · Traditional Survey Firms (if they don't adapt)
- · Datasets based on poorly validated LLM simulations
LLMs can reliably simulate individual human responses across different surveys.
The cost and speed of generating human-like survey data decrease dramatically, making large-scale behavioral studies more accessible.
Ethical and regulatory questions arise regarding the use of synthetic human data for influencing public opinion or policy, blurring lines between simulation and reality.
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Read at arXiv cs.AI