# Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations

arXiv: https://arxiv.org/abs/2502.07068 · HTML: https://mlsanity.com/paper/2502.07068/

## Key facts

- Cited by 53 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 10 Feb 2025.

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

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [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)
- [Towards Measuring the Representation of Subjective Global Opinions in Language Models](https://mlsanity.com/paper/2306.16388/) (2023)
- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
- [Probing Pre-Trained Language Models for Cross-Cultural Differences in Values](https://mlsanity.com/paper/2203.13722/) (2022)
- [Investigating Cultural Alignment of Large Language Models](https://mlsanity.com/paper/2402.13231/) (2024)
- [Stop Measuring Calibration When Humans Disagree](https://mlsanity.com/paper/2210.16133/) (2022)
- [Can multiple-choice questions really be useful in detecting the abilities of LLMs?](https://mlsanity.com/paper/2403.17752/) (2024)
- [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)
- [Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personas](https://mlsanity.com/paper/2408.06929/) (2024)
- [Assessing LLMs for Moral Value Pluralism](https://mlsanity.com/paper/2312.10075/) (2023)
- [Investigating Uncertainty Calibration of Aligned Language Models under the Multiple-Choice Setting](https://mlsanity.com/paper/2310.11732/) (2023)
- [Interpreting Predictive Probabilities: Model Confidence or Human Label Variation?](https://mlsanity.com/paper/2402.16102/) (2024)
- [On the Calibration of Multilingual Question Answering LLMs](https://mlsanity.com/paper/2311.08669/) (2023)
- [Revealing Fine-Grained Values and Opinions in Large Language Models](https://mlsanity.com/paper/2406.19238/) (2024)

## Built on by (most cited first, 30 of 53)

- [Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions](https://mlsanity.com/paper/2502.16761/) (2025)
- [Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions](https://mlsanity.com/paper/2502.20897/) (2025)
- [Valid Survey Simulations with Limited Human Data: The Roles of Prompting, Fine-Tuning, and Rectification](https://mlsanity.com/paper/2510.11408/) (2025)
- [The threat of analytic flexibility in using large language models to simulate human data](https://mlsanity.com/paper/2509.13397/) (2025)
- [LLM-Based Social Simulations Require a Boundary](https://mlsanity.com/paper/2506.19806/) (2025)
- [Assessing the Reliability of Persona-Conditioned LLMs as Synthetic Survey Respondents](https://mlsanity.com/paper/2602.18462/) (2026)
- [Revisiting LLM Value Probing Strategies: Are They Robust and Expressive?](https://mlsanity.com/paper/2507.13490/) (2025)
- [Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling](https://mlsanity.com/paper/2506.09998/) (2025)
- [Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generation](https://mlsanity.com/paper/2509.06337/) (2025)
- [Zhyper: Factorized Hypernetworks for Conditioned LLM Fine-Tuning](https://mlsanity.com/paper/2510.19733/) (2025)
- [Distribution Shift Alignment Helps LLMs Simulate Survey Response Distributions](https://mlsanity.com/paper/2510.21977/) (2025)
- [German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies](https://mlsanity.com/paper/2511.21722/) (2025)
- [When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses](https://mlsanity.com/paper/2607.26348/) (2026)
- [LLMs as Strategic Actors: Behavioral Alignment, Risk Calibration, and Argumentation Framing in Geopolitical Simulations](https://mlsanity.com/paper/2603.02128/) (2026)
- [When Can Digital Personas Reliably Approximate Human Survey Findings?](https://mlsanity.com/paper/2605.10659/) (2026)
- [Beyond the Mean: Three-Axis Fidelity for Aligning LLM-Based Survey Simulators from Small Pilot Data](https://mlsanity.com/paper/2606.28963/) (2026)
- [From Word to World: Evaluate and Mitigate Culture Bias in LLMs via Word Association Test](https://mlsanity.com/paper/2505.18562/) (2025)
- [Improving the Distributional Alignment of LLMs using Supervision](https://mlsanity.com/paper/2507.00439/) (2025)
- [Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble](https://mlsanity.com/paper/2509.11311/) (2025)
- [Predicting Effects, Missing Distributions: Evaluating LLMs as Human Behavior Simulators in Operations Management](https://mlsanity.com/paper/2510.03310/) (2025)
- [Tailored untruths: How personalisation challenges LLM safeguards](https://mlsanity.com/paper/2510.12993/) (2025)
- [Model-Free Assessment of Simulator Fidelity via Quantile Curves](https://mlsanity.com/paper/2512.05024/) (2025)
- [SYN-DIGITS: A Synthetic Control Framework for Calibrated Digital Twin Simulation](https://mlsanity.com/paper/2604.07513/) (2026)
- [Preregistration for Experiments with AI Agents](https://mlsanity.com/paper/2606.11217/) (2026)
- [Too Open for Opinion? Embracing Open-Endedness in Large Language Models for Social Simulation](https://mlsanity.com/paper/2510.13884/) (2025)
- [Graph-Based Alternatives to LLMs for Human Simulation](https://mlsanity.com/paper/2511.02135/) (2025)
- [RADIUS: Ranking, Distribution, and Significance - A Comprehensive Alignment Suite for Survey Simulation](https://mlsanity.com/paper/2603.19002/) (2026)
- [Statistical realism is not evidence that LLMs can estimate treatment effects in social science experiments](https://mlsanity.com/paper/2604.02458/) (2026)
- [When Seekers Are Hard to Help: Evaluating Emotional Support Dialogue Systems in Worst-Case Interactions](https://mlsanity.com/paper/2605.28228/) (2026)
- [In-Context Learning for the Imputation of Public Opinion Data with Large Language Models](https://mlsanity.com/paper/2606.09351/) (2026)

## Abstract (arXiv)

Large-scale surveys are essential tools for informing social science research and policy, but running surveys is costly and time-intensive. If we could accurately simulate group-level survey results, this would therefore be very valuable to social science research. Prior work has explored the use of large language models (LLMs) for simulating human behaviors, mostly through prompting. In this paper, we are the first to specialize LLMs for the task of simulating survey response distributions. As a testbed, we use country-level results from two global cultural surveys. We devise a fine-tuning method based on first-token probabilities to minimize divergence between predicted and actual response distributions for a given question. Then, we show that this method substantially outperforms other methods and zero-shot classifiers, even on unseen questions, countries, and a completely unseen survey. While even our best models struggle with the task, especially on unseen questions, our results demonstrate the benefits of specialization for simulation, which may accelerate progress towards sufficiently accurate simulation in the future.