# Random Silicon Sampling: Simulating Human Sub-Population Opinion Using a Large Language Model Based on Group-Level Demographic Information

arXiv: https://arxiv.org/abs/2402.18144 · HTML: https://mlsanity.com/paper/2402.18144/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 74 references that are in the map.
- First posted on arXiv 28 Feb 2024.

## Builds on (most cited first, 28 of 28)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Discovering Language Model Behaviors with Model-Written Evaluations](https://mlsanity.com/paper/2212.09251/) (2022)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [AGIEval: A Human-Centric Benchmark for Evaluating Foundation Models](https://mlsanity.com/paper/2304.06364/) (2023)
- [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)
- [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022)
- [Toxicity in ChatGPT: Analyzing Persona-assigned Language Models](https://mlsanity.com/paper/2304.05335/) (2023)
- [From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://mlsanity.com/paper/2305.08283/) (2023)
- [Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://mlsanity.com/paper/2305.18189/) (2023)
- [Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs](https://mlsanity.com/paper/2311.04892/) (2023)
- [The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation](https://mlsanity.com/paper/2301.01768/) (2023)
- [Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Do](https://mlsanity.com/paper/2103.11790/) (2021)
- [In-Context Impersonation Reveals Large Language Models' Strengths and Biases](https://mlsanity.com/paper/2305.14930/) (2023)
- [Aligning Language Models to User Opinions](https://mlsanity.com/paper/2305.14929/) (2023)
- [CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations](https://mlsanity.com/paper/2310.11501/) (2023)
- [NLPositionality: Characterizing Design Biases of Datasets and Models](https://mlsanity.com/paper/2306.01943/) (2023)
- [Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identity](https://mlsanity.com/paper/2209.12106/) (2022)
- [Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms](https://mlsanity.com/paper/2310.05984/) (2023)
- [Identifying and Manipulating the Personality Traits of Language Models](https://mlsanity.com/paper/2212.10276/) (2022)
- [CommunityLM: Probing Partisan Worldviews from Language Models](https://mlsanity.com/paper/2209.07065/) (2022)
- [Large Language Models Can Be Used to Estimate the Latent Positions of Politicians](https://mlsanity.com/paper/2303.12057/) (2023)
- [Language Models Trained on Media Diets Can Predict Public Opinion](https://mlsanity.com/paper/2303.16779/) (2023)
- [Susceptibility to Influence of Large Language Models](https://mlsanity.com/paper/2303.06074/) (2023)
- [Large Language Models as Subpopulation Representative Models: A Review](https://mlsanity.com/paper/2310.17888/) (2023)
- [Demonstrations of the Potential of AI-based Political Issue Polling](https://mlsanity.com/paper/2307.04781/) (2023)
- [Can Large Language Models Capture Public Opinion about Global Warming? An Empirical Assessment of Algorithmic Fidelity and Bias](https://mlsanity.com/paper/2311.00217/) (2023)

## Built on by (most cited first, 30 of 49)

- [AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society](https://mlsanity.com/paper/2502.08691/) (2025)
- [From Individual to Society: A Survey on Social Simulation Driven by Large Language Model-based Agents](https://mlsanity.com/paper/2412.03563/) (2024)
- [Large Language Models Assume People are More Rational than We Really are](https://mlsanity.com/paper/2406.17055/) (2024)
- [Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations](https://mlsanity.com/paper/2502.07068/) (2025)
- [Beyond Demographics: Aligning Role-playing LLM-based Agents Using Human Belief Networks](https://mlsanity.com/paper/2406.17232/) (2024)
- [Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personas](https://mlsanity.com/paper/2408.06929/) (2024)
- [The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models](https://mlsanity.com/paper/2406.11096/) (2024)
- [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)
- [Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case Study](https://mlsanity.com/paper/2412.13169/) (2024)
- [Beyond Static Responses: Multi-Agent LLM Systems as a New Paradigm for Social Science Research](https://mlsanity.com/paper/2506.01839/) (2025)
- [Large Language Models as Virtual Survey Respondents: Evaluating Sociodemographic Response Generation](https://mlsanity.com/paper/2509.06337/) (2025)
- [MatrAIx: Simulating the World with 8.3 Billion Persona Agents](https://mlsanity.com/paper/2608.04205/) (2026)
- [Extracting Affect Aggregates from Longitudinal Social Media Data with Temporal Adapters for Large Language Models](https://mlsanity.com/paper/2409.17990/) (2024)
- [ValueSim: Generating Backstories to Model Individual Value Systems](https://mlsanity.com/paper/2505.23827/) (2025)
- [Restoring Heterogeneity in LLM-based Social Simulation: An Audience Segmentation Approach](https://mlsanity.com/paper/2604.06663/) (2026)
- [Kiss up, Kick down: Exploring Behavioral Changes in Multi-modal Large Language Models with Assigned Visual Personas](https://mlsanity.com/paper/2410.03181/) (2024)
- [LLM Generated Distribution-Based Prediction of US Electoral Results, Part I](https://mlsanity.com/paper/2411.03486/) (2024)
- [Beyond the Mean: Three-Axis Fidelity for Aligning LLM-Based Survey Simulators from Small Pilot Data](https://mlsanity.com/paper/2606.28963/) (2026)
- [The creative psychometric item generator: a framework for item generation and validation using large language models](https://mlsanity.com/paper/2409.00202/) (2024)
- [Improving the Distributional Alignment of LLMs using Supervision](https://mlsanity.com/paper/2507.00439/) (2025)
- [Exploring Large Language Model Agents for Piloting Social Experiments](https://mlsanity.com/paper/2508.08678/) (2025)
- [Intelligent Computing Social Modeling and Methodological Innovations in Political Science in the Era of Large Language Models](https://mlsanity.com/paper/2410.16301/) (2024)
- [Can Large Language Models Predict Associations Among Human Attitudes?](https://mlsanity.com/paper/2503.21011/) (2025)
- [Reward Model Perspectives: Whose Opinions Do Reward Models Reward?](https://mlsanity.com/paper/2510.06391/) (2025)
- [Prompt Optimization Across Multiple Agents for Representing Diverse Human Populations](https://mlsanity.com/paper/2510.07064/) (2025)
- [Mitigating Social Desirability Bias in Random Silicon Sampling](https://mlsanity.com/paper/2512.22725/) (2025)
- [RADIUS: Ranking, Distribution, and Significance - A Comprehensive Alignment Suite for Survey Simulation](https://mlsanity.com/paper/2603.19002/) (2026)
- [Characterizing the ability of LLMs to recapitulate Americans' distributional responses to public opinion polling questions across political issues](https://mlsanity.com/paper/2603.20229/) (2026)
- [Improving Cross-Cultural Survey Simulation with Calibrated Value Personas](https://mlsanity.com/paper/2605.16193/) (2026)

## Abstract (arXiv)

Large language models exhibit societal biases associated with demographic information, including race, gender, and others. Endowing such language models with personalities based on demographic data can enable generating opinions that align with those of humans. Building on this idea, we propose "random silicon sampling," a method to emulate the opinions of the human population sub-group. Our study analyzed 1) a language model that generates the survey responses that correspond with a human group based solely on its demographic distribution and 2) the applicability of our methodology across various demographic subgroups and thematic questions. Through random silicon sampling and using only group-level demographic information, we discovered that language models can generate response distributions that are remarkably similar to the actual U.S. public opinion polls. Moreover, we found that the replicability of language models varies depending on the demographic group and topic of the question, and this can be attributed to inherent societal biases in the models. Our findings demonstrate the feasibility of mirroring a group's opinion using only demographic distribution and elucidate the effect of social biases in language models on such simulations.