When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses
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. gener
Record details
Published: 28 July 2026
Source: arXiv cs.HC
Category: Research
Topics: Regulation
Retrieved: 30 July 2026
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How to cite this record
ethics.ai (28 July 2026), “When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses,” evidence record 14831, https://ethics.ai/record/14831 (originally published by arXiv cs.HC).
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