# Characterizing the ability of LLMs to recapitulate Americans' distributional responses to public opinion polling questions across political issues

arXiv: https://arxiv.org/abs/2603.20229 · HTML: https://mlsanity.com/paper/2603.20229/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 6 Mar 2026.

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

- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [Matryoshka Representation Learning](https://mlsanity.com/paper/2205.13147/) (2022)
- [LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals](https://mlsanity.com/paper/2411.10109/) (2024)
- [Who is GPT-3? An Exploration of Personality, Values and Demographics](https://mlsanity.com/paper/2209.14338/) (2022)
- [What Large Language Models Know and What People Think They Know](https://mlsanity.com/paper/2401.13835/) (2024)
- [Questioning the Survey Responses of Large Language Models](https://mlsanity.com/paper/2306.07951/) (2023)
- [Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions](https://mlsanity.com/paper/2502.16761/) (2025)
- [Random Silicon Sampling: Simulating Human Sub-Population Opinion Using a Large Language Model Based on Group-Level Demographic Information](https://mlsanity.com/paper/2402.18144/) (2024)
- [Large Language Models for Market Research: A Data-augmentation Approach](https://mlsanity.com/paper/2412.19363/) (2024)
- [Can We Trust LLMs? Mitigate Overconfidence Bias in LLMs through Knowledge Transfer](https://mlsanity.com/paper/2405.16856/) (2024)
- [Demonstrations of the Potential of AI-based Political Issue Polling](https://mlsanity.com/paper/2307.04781/) (2023)
- [Can LLMs Help Predict Elections? (Counter)Evidence from the World's Largest Democracy](https://mlsanity.com/paper/2405.07828/) (2024)
- [United in Diversity? Contextual Biases in LLM-Based Predictions of the 2024 European Parliament Elections](https://mlsanity.com/paper/2409.09045/) (2024)
- [Artificially Intelligent Opinion Polling](https://mlsanity.com/paper/2309.06029/) (2023)
- [LLM Generated Distribution-Based Prediction of US Electoral Results, Part I](https://mlsanity.com/paper/2411.03486/) (2024)

## Built on by (most cited first, 1 of 1)

- [ChronoCulture: Evaluating Cultural Awareness Across Time in Large Language Models](https://mlsanity.com/paper/2610.20472/) (2026)

## Abstract (arXiv)

Traditional survey-based political issue polling is becoming less tractable due to increasing costs and risk of bias associated with growing non-response rates and declining coverage of key demographic groups. With researchers and pollsters seeking alternatives, Large Language Models have drawn attention for their potential to augment human population studies in polling contexts. We propose and implement a new framework for anticipating human responses on multiple-choice political issue polling questions by directly prompting an LLM to predict a distribution of responses. By comparison to a large and high quality issue poll of the US population, the Cooperative Election Study, we evaluate how the accuracy of this framework varies across a range of demographics and questions on a variety of topics, as well as how this framework compares to previously proposed frameworks where LLMs are repeatedly queried to simulate individual respondents. We find the proposed framework consistently exhibits more accurate predictions than individual querying at significantly lower cost. In addition, we find the performance of the proposed framework varies much more systematically and predictably across demographics and questions, making it possible for those performing AI polling to better anticipate model performance using only information available before a query is issued.