# Generating Public Health Responses using Survey-Augmented Large Language Models

arXiv: https://arxiv.org/abs/2606.21820 · HTML: https://mlsanity.com/paper/2606.21820/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 44 references that are in the map.
- First posted on arXiv 20 Jun 2026.

## Builds on (most cited first, 16 of 16)

- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions](https://mlsanity.com/paper/2502.16761/) (2025)
- [TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation](https://mlsanity.com/paper/2410.20626/) (2024)
- [Epidemic Modeling with Generative Agents](https://mlsanity.com/paper/2307.04986/) (2023)
- [LLMs Can Generate a Better Answer by Aggregating Their Own Responses](https://mlsanity.com/paper/2503.04104/) (2025)
- [Fewer is More: Boosting LLM Reasoning with Reinforced Context Pruning](https://mlsanity.com/paper/2312.08901/) (2023)
- [HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection](https://mlsanity.com/paper/2408.02927/) (2024)
- [How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective](https://mlsanity.com/paper/2502.17773/) (2025)
- [Digital Twins as Funhouse Mirrors: Five Key Distortions](https://mlsanity.com/paper/2509.19088/) (2025)
- [UniPredict: Large Language Models are Universal Tabular Classifiers](https://mlsanity.com/paper/2310.03266/) (2023)
- [Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generation](https://mlsanity.com/paper/2509.06337/) (2025)
- [Llms, Virtual Users, and Bias: Predicting Any Survey Question Without Human Data](https://mlsanity.com/paper/2503.16498/) (2025)
- [Can Large Language Models Predict Associations Among Human Attitudes?](https://mlsanity.com/paper/2503.21011/) (2025)

## Abstract (arXiv)

Epidemiological models often rely on survey data to represent how individuals make health-related decisions, such as whether to vaccinate or adopt protective behaviors. However, repeated large-scale surveys are costly, time-consuming, and limited in the range of scenarios they can capture. In this work, we investigate whether large language models (LLMs) can generate synthetic survey responses that reproduce patterns observed in real populations. Using longitudinal data from the FluPaths surveys, we first identify groups associated with broadly positive or negative attitudes toward vaccination through clustering analysis. We then evaluate several LLMs using a cluster-informed prompting approach to generate synthetic survey responses across multiple epidemic waves. Across models, the synthetic data generally reproduce the distributions of demographic characteristics, vaccination-related beliefs, risk perceptions, and health behaviors observed in the survey data. However, they are less successful at capturing how these factors vary together within respondents. Some models reproduce group-level vaccination trends more reliably than others, although performance varies across waves. We also trained a classifier to distinguish real from synthetic records and found that the generated responses remained identifiable as synthetic. Overall, our findings suggest that LLM-generated survey data may provide a useful tool for exploratory data augmentation and we hope that it could support agent-based epidemic modeling approaches. However, the generated data should not be treated as a substitute for human survey data without further methodological improvements and validation.