Related papers: Large Language Models (LLMs) as Agents for Augmented Democracy

Large Language Models (LLMs) as Agents for Augmented Democracy

URL: http://arxiv.org/abs/2405.03452v3
Date: Tue, 30 Jul 2024 09:51:41 GMT
Title: Large Language Models (LLMs) as Agents for Augmented Democracy
Authors: Jairo Gudiño-Rosero, Umberto Grandi, César A. Hidalgo,
Abstract summary: We explore an augmented democracy system built on off-the-shelf LLMs fine-tuned to augment data on citizen's preferences. 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.
Score: 6.491009626125319
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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