Large Language Models Can Be Used to Estimate the Ideologies of Politicians in a Zero-Shot Learning Setting
By Patrick Y. Wu, Joshua A. Tucker, Jonathan Nagler, Solomon Messing. 2023
28 citations recorded by the source corpus.
Subfields: General Social Sciences, Political Science and International Relations, Sociology and Political Science, Large Language Models, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Political Science and International Relations
In Sociology and Political Science
- Fabrizio Gilardi
- Martino Maggetti
- Hung-yi Lee
- Cheng-Han Chiang
- Christel Koop
- Chris Hanretty
- M. Lodge
In Natural Language Processing
Citations in the corpus
- ChatGPT outperforms crowd workers for text-annotation tasks (2023, background): “LLMs have been shown to perform very well for a wide range of purposes, including ideological scaling (Wu et al., 2023), the classification of legislative proposals (Nay, 2023), the resolution of cognitive psychology tasks (Binz and Schulz, 2023), and the simulation of human samples for survey resea”