发表机构
GESIS - Leibniz Institute for the Social Sciences; Heidelberg University; Heinrich Heine University of Düsseldorf; RWTH Aachen University（莱布尼茨社会科学研究所（GESIS）; 海德堡大学; 杜塞尔多夫海因里希·海涅大学; 亚琛工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出混合面板概念，构建首个试点，将人类与LLMs结合用于调查，旨在解决调查的响应率、成本等问题，为调查研究的人机协作提供新方向。
AI 中文摘要
大规模人口调查是获取可靠社会与科学洞见的核心方式，但面临诸多重大挑战，包括响应率下降、数据采集成本上升、数据采集与交付之间的延迟过长，以及无响应偏差风险。人工智能（AI）的发展为AI支持的调查基础设施开辟了新机遇，其目标是在不降低数据质量的前提下克服这些挑战。混合面板是一种极具前景的AI支持调查基础设施，我们已构建了首个试点版本。混合面板是一种纵向的AI支持调查，它允许迭代改进大语言模型（LLMs）与其旨在模拟的人群之间的一致性，并利用这些错误为下一轮调查的设计与实施提供依据（例如，为参与者招募、向参与者分配问题提供参考）。它将人类参与者和LLMs作为其设计的基本要素纳入其中。在本研究札记中，我们通过提供定义并概述从数据采集到数据验证的总体框架来介绍混合面板的概念。我们详细阐述了首个试点研究的结果，以说明我们识别出的混合面板所面临的（开放性）挑战。
英文摘要
Large-scale population surveys are essential for generating robust social and scientific insights, yet they face significant challenges, including declining response rates, increasing data collection costs, long delays between data collection and data provision, and the risk of nonresponse bias. Advances in artificial intelligence (AI) have opened up new opportunities for AI-supported survey infrastructures where the goal is to overcome these challenges without limiting the data quality. A promising AI-enabled survey infrastructure for which we build a first pilot is a hybrid panel. A hybrid panel is a longitudinal AI-enabled survey which allows to iteratively improve the alignment between large language models (LLMs) and the population they aim to simulate and use the errors to inform the design and implementation of the next survey wave (e.g., inform the participant recruitment, assignment of questions to participants). It incorporates both human participants and LLMs as fundamental elements of its design. In this research note, we introduce the concept of a hybrid panel by providing a definition and outlining an overarching framework, spanning data collection to data validation. We detail results from a first pilot study to illustrate (open) challenges that we identify for hybrid panels.
Comments15 pages, under review
