In-Context Learning for the Imputation of Public Opinion Data with Large Language Models
Researchers have developed a new method for imputing missing public opinion data using large language models (LLMs) through in-context learning (ICL). This approach was tested on survey data and showed consistent error reduction compared to traditional statistical methods like MICE PMM. The best-performing ICL method, utilizing a gpt-oss-120b model with 100 examples, achieved narrower confidence intervals and improved aggregate coverage, particularly under non-random missingness. AI
IMPACT This research demonstrates a novel application of LLMs for improving the accuracy and efficiency of public opinion data imputation, potentially impacting survey methodology and analysis.