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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 synth
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