AI-Augmented Surveys: Leveraging Large Language Models and S…

Established


35
Citations
0
Refs
2023
Year
Search Works
Work Details
  • DOI 10.48550/arxiv.2305.09620
  • OpenAlex ID W4377009982
  • Type Preprint
  • Language EN
  • Published 16 May 2023
  • Source arXiv (Cornell University)
  • Publisher Cornell University
  • Countries 1
  • Institutions 2
  • Added 19 May 2023
  • Updated 29 Oct 2026
MENU
0

AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction

Preprint 2023 EN Green

Nationally representative surveys track public opinion, yet they ask only a limited set of questions each year, limiting its potential to capture historical changes. To fill this gap, we develop a large language model (LLM)-based framework for predicting missing responses in repeated cross-sectional surveys by incorporating embeddings for questions, respondents, and survey periods. We introduce tw…

35

Citations

0

References

2

Authors

0.00

FWCI

Citations by Year
Last 4 years
Topics
Keywords
Authors & Affiliations
2 authors
#AuthorInstitutionsORCID
1 Kim, Junsol University of Chicago (US) —
2 Byungkyu Lee New York University (US)

Data: OpenAlex