Can Large Language Models Revolutionize Survey Research? Experiments with Disaster Preparedness Responses
The article discusses the potential of large language models (LLMs) to address challenges in survey research, particularly in disaster preparedness contexts. It presents a five-stage framework for integrating LLMs into the survey process and evaluates their performance against traditional methods. The findings suggest that LLMs can improve data quality and reduce bias in survey responses.
- ▪Survey research is facing challenges such as declining response rates and sample bias.
- ▪The study evaluates a framework for integrating large language models into the survey workflow using a disaster preparedness survey.
- ▪The proposed Anchored Marginal Theory-Informed LLM outperformed traditional imputation methods in terms of root mean square error.
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Computer Science > Artificial Intelligence arXiv:2605.19229 (cs) [Submitted on 19 May 2026] Title:Can Large Language Models Revolutionize Survey Research? Experiments with Disaster Preparedness Responses Authors:Yan Wang, Ziyi Guo, Christopher McCarty View a PDF of the paper titled Can Large Language Models Revolutionize Survey Research? Experiments with Disaster Preparedness Responses, by Yan Wang and 2 other authors View PDF HTML (experimental) Abstract:Survey research faces mounting structural challenges: declining response rates, sample bias, block-wise missingness among at-risk respondents, and AI-assisted fraudulent completions in online panels.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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