Large Language Models (LLMs) as Agents for Augmented Democracy
<|reference_start|>Large Language Models (LLMs) as Agents for Augmented Democracy: 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.<|reference_end|>
@article{gudiño-rosero2024large,
    title={Large Language Models (LLMs) as Agents for Augmented Democracy},
    author={Jairo Gudi~no-Rosero, Umberto Grandi, C'esar A. Hidalgo},
    journal={arXiv preprint arXiv:2405.03452},
    year={2024},
    doi={10.1098/rsta},
    archivePrefix={arXiv},
    eprint={2405.03452},
    primaryClass={cs.CY cs.AI cs.CL}
}