评估生成式智能体在众包事实核查中的潜力
Assessing the Potential of Generative Agents in Crowdsourced Fact-Checking
AI总结：
本研究通过模拟多样化的生成式智能体群体，验证了其在众包事实核查中优于人类群体，表现出更高的真实性分类准确率、内部一致性及更低的偏见倾向，凸显其作为可扩展、可靠核查贡献者的潜力。
摘要：
在线错误信息的日益传播，催生了对可扩展、可靠的事实核查解决方案的迫切需求。众包事实核查——即由非专家评估声明真实性——提供了一种比专家核查更具成本效益的替代方案，尽管其存在质量参差不齐和偏见方面的担忧。受某些情境下有前景结果的鼓舞，X（原推特）、脸书和Instagram等主要平台已开始从集中式审核转向去中心化的、基于众包的方式。与此同时，大型语言模型（LLMs）的进展在核心事实核查任务中展现出强劲性能，包括声明检测和证据评估。然而，它们在众包工作流程中的潜在作用仍未得到探索。本文研究由LLM驱动的生成式智能体——即模拟人类行为和决策的自主实体——是否能够有意义地贡献于传统上由人类群体承担的事实核查任务。采用La Barbera等人（2024）的协议，我们模拟了具有不同人口统计和意识形态特征的生成式智能体群体。智能体检索证据，沿多个质量维度评估声明，并发布最终真实性判断。我们的结果表明，智能体群体在真实性分类方面优于人类群体，表现出更高的内部一致性，并且对社会和认知偏见的易感性降低。与人类相比，智能体更系统地依赖诸如准确性、精确性和信息量等有信息量的标准，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、一致且较少偏见的贡献者，在基于众包的事实核查系统中的潜力。
英文摘要：
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.
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