Silicon Sampling via Cross-Survey Transfer

📰 ArXiv cs.AI

Learn how to evaluate large language models for silicon sampling using cross-survey transfer for more accurate respondent-level prediction

advanced Published 7 Jul 2026

Action Steps

Apply cross-survey transfer to evaluate LLMs for silicon sampling
Use distributional comparisons to assess pattern matching
Implement individual-level prediction to assess coherent respondent-level prediction
Configure LLMs to simulate human survey respondents
Test the performance of LLMs using cross-survey transfer

Who Needs to Know This

Data scientists and survey researchers can benefit from this approach to improve the accuracy of their predictions and augment traditional survey research

Key Insight

💡 Cross-survey transfer is a more rigorous evaluation framework for assessing the performance of LLMs in silicon sampling

Key Takeaways

Learn how to evaluate large language models for silicon sampling using cross-survey transfer for more accurate respondent-level prediction

Full Article

Title: Silicon Sampling via Cross-Survey Transfer

Abstract:
arXiv:2607.03091v1 Announce Type: new 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

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