How can we use large language models (LLMs) to augment surveys? This paper
investigates three distinct applications of LLMs fine-tuned by nationally
representative surveys for opinion prediction -- missing data imputation,
retrodiction, and zero-shot prediction. We present a new methodological
framework that incorporates neural embeddings of survey questions, individual
beliefs, and temporal contexts to personalize LLMs in opinion prediction. Among
3,110 binarized opinions from 68,846 Americans in the General Social Survey
from 1972 to 2021, our best models based on Alpaca-7b excels in missing data
imputation (AUC = 0.87 for personal opinion prediction and ρ = 0.99 for
public opinion prediction) and retrodiction (AUC = 0.86, ρ = 0.98). These
remarkable prediction capabilities allow us to fill in missing trends with high
confidence and pinpoint when public attitudes changed, such as the rising
support for same-sex marriage. However, the models show limited performance in
a zero-shot prediction task (AUC = 0.73, ρ = 0.67), highlighting
challenges presented by LLMs without human responses. Further, we find that the
best models' accuracy is lower for individuals with low socioeconomic status,
racial minorities, and non-partisan affiliations but higher for ideologically
sorted opinions in contemporary periods. We discuss practical constraints,
socio-demographic representation, and ethical concerns regarding individual
autonomy and privacy when using LLMs for opinion prediction. This paper
showcases a new approach for leveraging LLMs to enhance nationally
representative surveys by predicting missing responses and trends