The growing spread of online misinformation has created an urgent need for scalable, reliable fact-checking solutions. Crowdsourced fact-checking - where non-experts evaluate claim veracity - offers a cost-effective alternative to expert verification, despite concerns about variability in quality and bias. Encouraged by promising results in certain contexts, major platforms such as X (formerly Twitter), Facebook, and Instagram have begun shifting from centralized moderation to decentralized, crowd-based approaches. In parallel, advances in Large Language Models (LLMs) have shown strong performance across core fact-checking tasks, including claim detection and evidence evaluation. However, their potential role in crowdsourced workflows remains unexplored. This paper investigates whether LLM-powered generative agents - autonomous entities that emulate human behavior and decision-making - can meaningfully contribute to fact-checking tasks traditionally reserved for human crowds. Using the protocol of La Barbera et al. (2024), we simulate crowds of generative agents with diverse demographic and ideological profiles. Agents retrieve evidence, assess claims along multiple quality dimensions, and issue final veracity judgments. Our results show that agent crowds outperform human crowds in truthfulness classification, exhibit higher internal consistency, and show reduced susceptibility to social and cognitive biases. Compared to humans, agents rely more systematically on informative criteria such as Accuracy, Precision, and Informativeness, suggesting a more structured decision-making process. Overall, our findings highlight the potential of generative agents as scalable, consistent, and less biased contributors to crowd-based fact-checking systems.

翻译：在线虚假信息的日益蔓延对可扩展、可靠的事实核查解决方案提出了迫切需求。众包事实核查——即由非专业人士评估声明的真实性——尽管存在质量和偏见方面的担忧，但仍为专家验证提供了一种经济高效的替代方案。受特定情境下积极成果的鼓舞，X（原Twitter）、Facebook和Instagram等主要平台已开始从集中式审核转向去中心化的众包模式。与此同时，大型语言模型（LLMs）在核心事实核查任务（包括声明检测和证据评估）中展现出卓越性能。然而，它们在众包工作流程中的潜在作用尚未得到探索。本文研究由LLM驱动的生成式智能体——模拟人类行为与决策的自主实体——是否能在传统上由人类众包承担的事实核查任务中发挥实质性作用。借鉴La Barbera等人（2024）的框架，我们模拟了具有多样化人口统计学和意识形态特征的生成式智能体群体。智能体检索证据，从多个质量维度评估声明，并给出最终的真实性判断。研究结果表明：智能体群体在真实性分类任务中表现优于人类群体，展现出更高的内部一致性，且对社会与认知偏见的敏感性更低。与人类相比，智能体更系统地依赖准确性、精确性和信息量等客观标准，表明其决策过程更具结构性。总体而言，我们的发现凸显了生成式智能体作为可扩展、一致且偏见更少的参与者，在众包事实核查系统中的巨大潜力。