Beyond Keywords: Leveraging Generative LLMs and Label Aggregation to Classify Economic Policy Uncertainty in News Articles

Abstract:This research describes the adaptation of Large Language Models (LLMs) for economic monitoring in the public sector to automatically determine whether an article discusses Economic Policy Uncertanity (EPU) and to identify its specific type. Previous studies either rely on keywords, which often result in a high count of false positives, or use machine learning approaches that require a large number of quality human labeled data that is costly and time consuming to acquire. In this study, we propose approaches based on weak supervision techniques, using generative LLMs to create synthetic labels through prompting, making the approach both cost-effective and scalable. Additionally, we propose methods for for multi-label and hierarchical classification of articles related to EPU.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.35856 [cs.LG]

(or arXiv:2609.35856v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.35856 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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