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Large Language Models (LLMs) as Agents for Augmented Democracy
- Publication Year :
- 2024
-
Abstract
- We explore an augmented democracy system built on off-the-shelf LLMs fine-tuned to augment data on citizen's preferences elicited over policies extracted from the government programs of the two main candidates of Brazil's 2022 presidential election. We use a train-test cross-validation setup 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 indicates that policy preference data augmented using LLMs can capture nuances that transcend party lines and represents a promising avenue of research for data augmentation.<br />Comment: 24 pages main manuscript with 4 figures. 13 pages of supplementary material
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2405.03452
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1098/rsta