Silicon Sampling via Cross-Survey Transfer

• URL: http://arxiv.org/abs/2607.03091v1
• Date: Fri, 03 Jul 2026 08:22:18 GMT
• Title: Silicon Sampling via Cross-Survey Transfer
• Abstract summary: Silicon sampling-using large language models (LLMs) to simulate human survey respondents has emerged as a promising approach for augmenting traditional survey research.<n>We propose a more rigorous evaluation framework in which an LLM is given a respondent's answers to one set of questions and must predict their answers to entirely different questions from the same survey.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Silicon sampling-using large language models (LLMs) to simulate human survey respondents-has emerged as a promising approach for augmenting traditional survey research. However, most evaluations rely on distributional comparisons rather than individual-level prediction, which risks conflating pattern matching with coherent respondent-level prediction. We propose cross-survey transfer, a more rigorous evaluation framework in which an LLM is given a respondent's answers to one set of questions and must predict their answers to entirely different questions from the same survey. Using data from the Taiwan Election and Democratization Study (TEDS) 2024, three open-weight LLMs (27B-120B parameters), and supervised machine learning baselines, we find that: (1) zero-shot LLMs achieve 52% accuracy on genuinely unseen items, closing to within 6 percentage points (pp) of a supervised random forest trained on same-population data; (2) a stable construct predictability hierarchy emerges, from 67% for partisan attitudes to 23% for sovereignty; and (3) variance collapse and safety alignment effects-two commonly cited LLM limitations-turn out to be more nuanced than previously reported, with variance collapse affecting supervised models as well and alignment effects varying dramatically across model families. These findings clarify both the promise and boundaries of silicon sampling.

Related papers

• Marginal Fidelity Does Not Establish User Simulation in Demographic Synthetic Survey Panels: Response Contracts, Support Collapse and Conditioning Failure [51.736723807086385]
  Population-marginal agreement is evidence about an elicitation contract and an estimand obtainable without simulated respondents, not evidence of individual simulation.<n>On nine aligned model-battery pairs, a no-persona population-prevalence query averages 6.27 MAE versus 12.39 for committed panels and wins all nine comparisons.
  arXiv  Detail & Related papers  (2026-09-07T10:22:22Z)
• Can Large Language Models Revolutionize Survey Research? Experiments with Disaster Preparedness Responses [6.004875368104112]
  Large language models (LLMs) have been proposed as a remedy, yet rigorous evaluations across the full survey workflow remain scarce.<n>We present and evaluate a five-stage framework for LLM integration covering questionnaire design, sample selection, pilot testing, missing-data imputation, and post-collection analysis.<n>We introduce a Protection Motivation Theory (PMT)-constrained co-occurrence knowledge graph and develop seven LLM configurations spanning zero-shot inference, retrieval-augmented baselines, and novel theory-informed variants.
  arXiv  Detail & Related papers  (2026-05-19T00:58:36Z)
• 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)
• RefineBench: Evaluating Refinement Capability of Language Models via Checklists [71.02281792867531]
  We evaluate two refinement modes: guided refinement and self-refinement.<n>In guided refinement, both proprietary LMs and large open-weight LMs can leverage targeted feedback to refine responses to near-perfect levels within five turns.<n>These findings suggest that frontier LMs require breakthroughs to self-refine their incorrect responses.
  arXiv  Detail & Related papers  (2025-11-27T07:20:52Z)
• Emulating Public Opinion: A Proof-of-Concept of AI-Generated Synthetic Survey Responses for the Chilean Case [0.0]
  Large Language Models (LLMs) offer promising avenues for methodological and applied innovations in survey research.<n>We evaluate the reliability of LLM-generated synthetic survey responses against ground-truth human responses from a Chilean public opinion probabilistic survey.
  arXiv  Detail & Related papers  (2025-09-11T21:43:59Z)
• Llms, Virtual Users, and Bias: Predicting Any Survey Question Without Human Data [0.0]
  We use Large Language Models (LLMs) to create virtual populations that answer survey questions.<n>We evaluate several LLMs-including GPT-4o, GPT-3.5, Claude 3.5-Sonnet, and versions of the Llama and Mistral models-comparing their performance to that of a traditional Random Forests algorithm.
  arXiv  Detail & Related papers  (2025-03-11T16:27:20Z)
• Uncertainty Quantification for LLM-Based Survey Simulations [9.303339416902995]
  We investigate the use of large language models (LLMs) to simulate human responses to survey questions.<n>Our approach converts imperfect LLM-simulated responses into confidence sets for population parameters.<n>A key innovation lies in determining the optimal number of simulated responses.
  arXiv  Detail & Related papers  (2025-02-25T02:07:29Z)
• Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions [4.020002996724124]
  Large language models (LLMs) predict survey responses in advance during the early stages of survey design.<n>We propose directly fine-tuning LLMs to predict response distributions by leveraging unique structural characteristics of survey data.<n>We show that fine-tuning on SubPOP greatly improves the match between LLM predictions and human responses across various subpopulations.
  arXiv  Detail & Related papers  (2025-02-24T00:31:33Z)
• 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)
• Uncertainty is Fragile: Manipulating Uncertainty in Large Language Models [79.76293901420146]
  Large Language Models (LLMs) are employed across various high-stakes domains, where the reliability of their outputs is crucial. Our research investigates the fragility of uncertainty estimation and explores potential attacks. We demonstrate that an attacker can embed a backdoor in LLMs, which, when activated by a specific trigger in the input, manipulates the model's uncertainty without affecting the final output.
  arXiv  Detail & Related papers  (2024-07-15T23:41:11Z)
• Cycles of Thought: Measuring LLM Confidence through Stable Explanations [53.15438489398938]
  Large language models (LLMs) can reach and even surpass human-level accuracy on a variety of benchmarks, but their overconfidence in incorrect responses is still a well-documented failure mode. We propose a framework for measuring an LLM's uncertainty with respect to the distribution of generated explanations for an answer.
  arXiv  Detail & Related papers  (2024-06-05T16:35:30Z)

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.