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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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.19229 |
| Publication time | Wed, 20 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-20T04:04:59.484Z |
| Last seen | 2026-05-20T04:04:59.484Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | nv53V-K5Z7T- |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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