---
title: "Silicon Sampling via Cross-Survey Transfer — Stuff That Spins"
description: "arXiv:2607.03091v1 Announce Type: new Abstract: Silicon sampling-using large language models (LLMs) to simulate human survey respondents-has emerged as a promi…"
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date: "2026-07-07T04:00:00+00:00"
modified: "2026-07-07T06:03:20.649491+00:00"
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# Silicon Sampling via Cross-Survey Transfer

**Source:** Unknown  
**Published:** July 7, 2026  
**Original:** https://arxiv.org/abs/2607.03091  

## On this page

- [Overview](#overview)

<a id="overview"></a>

## Overview

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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