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.
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arXiv:2607. 26348v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as synthetic users, stand-ins for human respondents whose simulated answers feed product, policy, and market decisions.
The paper introduces Population Fidelity, an evaluation framework for assessing how well large language models (LLMs) represent human population attitudes. It focuses on three dimensions: group-level accuracy, between-group variation, and the structure of that variation. Using the framework, the authors replicate a prior study on machine bias and test cultural fine-tuning, finding that while fine-tuning improves overall alignment, it does not enhance representation of within-population differences.
arXiv:2607. 25292v1 Announce Type: new Abstract: Silicon sampling uses language models as proxies for human survey respondents, treating each model call as an independent draw from the persona's response distribution.
The paper explores using large language models (LLMs) as AI respondents to convert policy documents into structured survey responses. It introduces a long-context in‑context learning pipeline that maps policy text to predefined survey categories such as policy instruments, target groups, and thematic areas, and includes a secondary LLM validation step. Evaluation on a multi‑country dataset shows high agreement (84‑95%) with human responses for structured indicators, though free‑text fields differ, indicating that hybrid human‑AI workflows can enhance policy monitoring efficiency while still requiring human oversight.
arXiv:2606.30085v2 Announce Type: replace Abstract: Large-language models have proven to be remarkable if inconsistent parrots of public attitudes and opinions. The extent to which LLMs are able to p...
arXiv:2603. 00059v3 Announce Type: replace-cross Abstract: How well can AI-derived synthetic research data replicate the responses of human participants?