Improving Cross-Cultural Survey Simulation with Calibrated Value Personas

• URL: http://arxiv.org/abs/2605.16193v1
• Date: Fri, 15 May 2026 17:10:50 GMT
• Title: Improving Cross-Cultural Survey Simulation with Calibrated Value Personas
• Abstract summary: Large language models (LLMs) are increasingly used to simulate human opinions and survey responses, but their ability to reproduce population responses across cultures remains limited.<n>We propose a value-based persona construction method that derives textual descriptors from survey responses capturing core cultural dimensions.<n>We show that our approach reduces prediction error across countries, with the largest improvements observed in underrepresented populations.
• Score: 2.5964189305343712
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are increasingly used to simulate human opinions and survey responses, but their ability to reproduce population responses across cultures remains limited. Existing persona-based prompting methods typically rely on sociodemographic or personality traits, which are only indirect proxies for the values that shape human responses. We propose a value-based persona construction method that derives textual descriptors from survey responses capturing core cultural dimensions. By sampling value profiles from target populations and aggregating LLM responses across personas, we obtain population-level predictions grounded in observed value distributions. We further introduce a calibration procedure that improves response diversity while preserving estimated opinions. We show that our approach reduces prediction error across countries, with the largest improvements observed in underrepresented populations. This substantially narrows the performance gap between countries aligned with dominant LLM priors and those that are less represented in training data, while also yielding response distributions that closely match human diversity.

Related papers

• Rethinking the Evaluation and Optimization of LLM-Based Social Simulation [56.18577997572273]
  We use the subjectivity coefficient to analyze how accuracy-based evaluation and hard-label training fail as subjectivity grows.<n>We construct SUBJSIM, a benchmark of 19,300 contexts covering 193 annotators and 100 subjective questions.<n>Results on SUBJSIM demonstrate the advantages of our method.
  arXiv  Detail & Related papers  (2026-08-20T06:26:03Z)
• Emulate or Estimate? The Divergent Strengths of Base and Post-Trained Language Models for Opinion Simulation [7.947622351777421]
  We show that some studies find promising alignment with human survey data, while others find persona collapse and weak demographic sensitivity.<n>We call the first task emulation, in which models generate individual responses that aggregate into a population distribution.<n>We call the second task estimation, in which models directly predict the population distribution.
  arXiv  Detail & Related papers  (2026-08-04T02:51:38Z)
• Evidence-based Distributional Alignment for Large Language Models [58.65469623911573]
  We propose Evi-DA, an evidence-based alignment technique that improves the fidelity and robustness of LLM-based distribution estimation.<n>Given a target country, Evi-DA retrieves related World Values Survey items and their answer distributions, predicts a coarse Welzel value signature for each option, and infers the country-conditioned answer distribution in a structured format.
  arXiv  Detail & Related papers  (2026-03-03T03:34:06Z)
• Assessing the Reliability of Persona-Conditioned LLMs as Synthetic Survey Respondents [0.4277616907160855]
  We use a large dataset of U.S. microdata to assess the impact of persona-conditioned simulations.<n>We find that persona prompting does not yield a clear aggregate improvement in survey alignment and, in many cases, significantly degrades performance.<n>Our findings highlight a key adverse impact of current persona-based simulation practices.
  arXiv  Detail & Related papers  (2026-02-06T15:13:59Z)
• Overstating Attitudes, Ignoring Networks: LLM Biases in Simulating Misinformation Susceptibility [7.616305266104683]
  Large language models (LLMs) are increasingly used as proxies for human judgment in computational social science.<n>We test whether LLM-simulated survey respondents can reproduce human patterns of misinformation belief and sharing.
  arXiv  Detail & Related papers  (2026-02-04T15:48:05Z)
• Prompts to Proxies: Emulating Human Preferences via a Compact LLM Ensemble [46.82793004650415]
  Large language models (LLMs) have demonstrated promise in emulating human-like responses across a range of tasks.<n>We propose a novel alignment framework that treats LLMs as agent proxies for human survey respondents.<n>We introduce P2P, a system that steers LLM agents toward representative behavioral patterns using structured prompt engineering, entropy-based sampling, and regression-based selection.
  arXiv  Detail & Related papers  (2025-09-14T15:08:45Z)
• MF-LLM: Simulating Population Decision Dynamics via a Mean-Field Large Language Model Framework [53.82097200295448]
  Mean-Field LLM (MF-LLM) is first to incorporate mean field theory into social simulation.<n>MF-LLM models bidirectional interactions between individuals and the population through an iterative process.<n> IB-Tune is a novel fine-tuning method inspired by the Information Bottleneck principle.
  arXiv  Detail & Related papers  (2025-04-30T12:41:51Z)
• Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations [49.908708778200115]
  We are the first to specialize large language models (LLMs) for simulating survey response distributions.<n>As a testbed, we use country-level results from two global cultural surveys.<n>We devise a fine-tuning method based on first-token probabilities to minimize divergence between predicted and actual response distributions.
  arXiv  Detail & Related papers  (2025-02-10T21:59:27Z)
• Using LLMs to Model the Beliefs and Preferences of Targeted Populations [4.0849074543032105]
  We consider the problem of aligning a large language model (LLM) to model the preferences of a human population. Modeling the beliefs, preferences, and behaviors of a specific population can be useful for a variety of different applications.
  arXiv  Detail & Related papers  (2024-03-29T15:58:46Z)
• Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation [68.9440575276396]
  This survey aims to provide an overview of the recent research that has leveraged human feedback to improve natural language generation. First, we introduce an encompassing formalization of feedback, and identify and organize existing research into a taxonomy following this formalization. Second, we discuss how feedback can be described by its format and objective, and cover the two approaches proposed to use feedback (either for training or decoding): directly using the feedback or training feedback models. Third, we provide an overview of the nascent field of AI feedback, which exploits large language models to make judgments based on a set of principles and minimize the need for
  arXiv  Detail & Related papers  (2023-05-01T17:36:06Z)
• Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach [7.467496975496821]
  We propose a two-way data integration strategy that integrates heterogeneous data from diverse populations and from multiple healthcare institutions. We show that the proposed method improves the estimation and prediction accuracy in underrepresented populations, and reduces the gap of model performance across populations.
  arXiv  Detail & Related papers  (2021-08-27T04:04:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.