Gudino, Jairo F., Grandi, Umberto and Hidalgo, Cesar Augusto (2024) Large language models (LLMs) as agents for augmented democracy. Philosophical Transactions of the Royal Society A, Vol. 382 (N°2285).

Abstract

We explore an augmented democracy system built on off-the-shelf large language models (LLMs) fine-tuned to augment data on citizens’ preferences elicited over policies extracted from the government programmes of the two main candidates of Brazil’s 2022 presidential election. We use a train-test cross-validation set-up to estimate the accuracy with which the LLMs predict both: a subject’s individual political choices and the aggregate preferences of the full sample of participants. At the individual level, we find that LLMs predict out of sample preferences more accurately than a ‘bundle rule’, which would assume that citizens always vote for the proposals of the candidate aligned with their self-reported political orientation. At the population level, we show that a probabilistic sample augmented by an LLM provides a more accurate estimate of the aggregate preferences of a population than the non-augmented probabilistic sample alone. Together, these results indicate that policy preference data augmented using LLMs can capture nuances that transcend party lines and represents a promising avenue of research for data augmentation.