AI RESEARCH
From Demographics to Survey Anchors: Evaluating LLM Agents for Modeling Retirement Attitudes
arXiv CS.CL
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ArXi:2605.16303v1 Announce Type: cross Large language models (LLM) agents may offer tools to predict human responses to surveys. A common technique for defining these agents uses only graphics, for example country, age, gender, employment status, income, education and marital status. We compare the predictive accuracy of graphic agents to that of survey agents defined with a larger set of in-domain survey responses.