Political-LLM: Large Language Models in Political Science
Abstrak
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/.
Topik & Kata Kunci
Penulis (47)
Lincan Li
Jiaqi Li
Catherine Chen
Fred Gui
Hongjian Yang
Chenxiao Yu
Zhengguang Wang
J. Cai
Junlong Zhou
Bolin Shen
Alexander Qian
Weixin Chen
Zhongkai Xue
Lichao Sun
Lifang He
Hanjie Chen
Kaize Ding
Zijia Du
Fangzhou Mu
Jiaxin Pei
Jieyu Zhao
Swabha Swayamdipta
Willie Neiswanger
Hua Wei
Xiyang Hu
Shixiang Zhu
Tian-Xiang Chen
Ying-Liang Lu
Yang Shi
Li Qin
Tianfan Fu
Zhengzhong Tu
Yuzhe Yang
Jaemin Yoo
Jiaheng Zhang
Ryan A. Rossi
Liang Zhan
Liangxuan Zhao
Emilio Ferrara
Yan Liu
Furong Huang
Xiangliang Zhang
L. Rothenberg
Shuiwang Ji
Philip S. Yu
Yue Zhao
Yushun Dong
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Total Sitasi
- 31×
- Sumber Database
- Semantic Scholar
- DOI
- 10.48550/arXiv.2412.06864
- Akses
- Open Access ✓