arXiv 2412.068642024-12-09cs.CLcs.AI

Political-LLM：政治学中的大语言模型

Political-LLM: Large Language Models in Political Science

Florida State University（佛罗里达州立大学）；University of Southern California（南加利福尼亚大学）；Louisiana State University（路易斯安那州立大学）；University of Virginia（弗吉尼亚大学）；University of Pennsylvania（宾夕法尼亚大学）；New York University（纽约大学）；Oxford University（牛津大学）；Lehigh University（理海大学）；Rice University（莱斯大学）；Northwestern University（西北大学）；NVIDIA（英伟达公司）

Lincan Li, Jiaqi Li, Catherine Chen, Fred Gui, Hongjia Yang, Chenxiao Yu, Zhengguang Wang, Jianing Cai, Junlong Aaron Zhou, Bolin Shen, Alex Qian, Weixin Chen, … 展开作者

Lincan Li, Jiaqi Li, Catherine Chen, Fred Gui, Hongjia Yang, Chenxiao Yu, Zhengguang Wang, Jianing Cai, Junlong Aaron Zhou, Bolin Shen, Alex Qian, Weixin Chen, Zhongkai Xue, Lichao Sun, Lifang He, Hanjie Chen, Kaize Ding, Zijian Du, Fangzhou Mu, Jiaxin Pei, Jieyu Zhao, Swabha Swayamdipta, Willie Neiswanger, Hua Wei, Xiyang Hu, Shixiang Zhu, Tianlong Chen, Yingzhou Lu, Yang Shi, Lianhui Qin, Tianfan Fu, Zhengzhong Tu, Yuzhe Yang, Jaemin Yoo, Jiaheng Zhang, Ryan Rossi, Liang Zhan, Liang Zhao, Emilio Ferrara, Yan Liu, Furong Huang, Xiangliang Zhang, Lawrence Rothenberg, Shuiwang Ji, Philip S. Yu, Yue Zhao, Yushun Dong

备注：: 54 Pages, 9 Figures

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AI总结：

本文提出Political-LLM框架，从政治学与计算方法双视角梳理LLM在计算政治学中的应用、技术进展、挑战与未来方向。

摘要：

近年来，大语言模型（LLM）已被广泛应用于选举预测、情感分析、政策影响评估和错误信息检测等政治学任务。与此同时，系统理解LLM如何进一步革新该领域的需求也变得迫切。在本工作中，我们——一个跨越计算机科学与政治学的多学科研究团队——提出首个名为Political-LLM的原则性框架，以促进对将LLM融入计算政治学的全面理解。具体而言，我们首先介绍一种基础分类法，将现有探索划分为两个视角：政治学视角和计算方法视角。在政治学视角下，我们强调LLM在自动化预测与生成任务、模拟行为动态，以及通过反事实生成等工具改进因果推断方面的作用；在计算视角下，我们介绍面向政治情境定制的LLM在数据准备、微调和评估方法方面的进展。我们识别出关键挑战与未来方向，强调开发领域专用数据集、解决偏见与公平问题、纳入人类专业知识，以及重新定义评估标准以契合计算政治学的独特要求。Political-LLM力求成为研究人员的指南，以推动在政治学中明智、合乎伦理且富有成效地使用人工智能。我们的在线资源位于：http://political-llm.org/。

英文摘要：

In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and misinformation detection. Meanwhile, the need to systematically understand how LLMs can further revolutionize the field also becomes urgent. In this work, we--a multidisciplinary team of researchers spanning computer science and political science--present the first principled framework termed Political-LLM to advance the comprehensive understanding of integrating LLMs into computational political science. Specifically, we first introduce a fundamental taxonomy classifying the existing explorations into two perspectives: political science and computational methodologies. In particular, from the political science perspective, we highlight the role of LLMs in automating predictive and generative tasks, simulating behavior dynamics, and improving causal inference through tools like counterfactual generation; from a computational perspective, we introduce advancements in data preparation, fine-tuning, and evaluation methods for LLMs that are tailored to political contexts. We identify key challenges and future directions, emphasizing the development of domain-specific datasets, addressing issues of bias and fairness, incorporating human expertise, and redefining evaluation criteria to align with the unique requirements of computational political science. Political-LLM seeks to serve as a guidebook for researchers to foster an informed, ethical, and impactful use of Artificial Intelligence in political science. Our online resource is available at: http://political-llm.org/.

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