标题： 迈向美国国会的数字孪生技术 显示英文标题

标题： Toward a digital twin of U.S. Congress

摘要： 本文提供证据表明，基于一组语言模型的美国国会议员虚拟模型满足数字孪生的定义。特别地，我们介绍了每日更新的数据集，该数据集包含了每位美国国会议员在其任期内发布的每条推文，并提供了高层次描述。我们证明了配备了这些数据特定子集的现代语言模型能够生成与实际发布的推文大体无法区分的推文。我们展示了如何利用生成的推文来预测投票行为，并量化议员跨越党派界限的可能性，从而帮助利益相关者分配资源，并可能影响现实世界的立法动态。最后，我们讨论了本分析的局限性和重要的扩展方向。 显示英文摘要

摘要： In this paper we provide evidence that a virtual model of U.S. congresspersons based on a collection of language models satisfies the definition of a digital twin. In particular, we introduce and provide high-level descriptions of a daily-updated dataset that contains every Tweet from every U.S. congressperson during their respective terms. We demonstrate that a modern language model equipped with congressperson-specific subsets of this data are capable of producing Tweets that are largely indistinguishable from actual Tweets posted by their physical counterparts. We illustrate how generated Tweets can be used to predict roll-call vote behaviors and to quantify the likelihood of congresspersons crossing party lines, thereby assisting stakeholders in allocating resources and potentially impacting real-world legislative dynamics. We conclude with a discussion of the limitations and important extensions of our analysis.