Do LLMs Ask the Right Questions? Evaluating GPT-Generated Surveys as Instruments for Measuring Social Attitudes

Abstract

Understanding human beliefs and social attitudes often relies on carefully designed survey instruments. Recent work has suggested that large language models (LLMs) could automate parts of this process by generating surveys at scale, raising questions about the comparability of such instruments to literature-grounded, human-designed surveys. We present a controlled empirical comparison between GPT-generated surveys and established survey baselines across three social domains: climate change, immigration, and diversity, equity, and inclusion (DEI). GPT-generated surveys were produced using a fixed prompting framework enforcing a 3×3 structure over beliefs, perceptions, and behaviors, while human baselines were assembled from validated instruments to match survey length and construct coverage. We collected responses from U.S.-based participants, who completed both survey types, allowing direct within-subject comparison. We analyze differences in response distributions, clustering behavior, and alignment with self-identified stances. Our results show that GPT-generated surveys capture the same dominant attitudinal divisions as human-designed instruments, while exhibiting differences in the resolution of belief structure and group separation. These findings suggest that LLM-generated surveys are suited for exploratory and large-scale analyses, and can be used to complement expert-designed instruments.

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