Zero-Shot Annotation by Large Language Model with Serial Correction of Mixed Label Corruption for Weakly Supervised Financial News Classification

Abstract

Multi-label classification of financial news is frequently affected by incomplete and noisy annotations, while obtaining expert-curated labels at scale is prohibitively expensive. This study proposes a weakly supervised classification framework that combines large language model (LLM) zero-shot annotation with a serial label-correction strategy. The framework first uses an LLM to generate initial weak labels and then refines them through a two-stage Correct→Clean procedure that recovers missing labels via centrality-weighted graph propagation before suppressing label noise. Systematic experiments on a financial subset of Reuters-21578 show that, under an extreme mixed-corruption setting with 80% missing labels and 15% noise labels, Correct→Clean increases the Micro-F1 from 0 to 0.6748. In an end-to-end evaluation, the proposed framework achieves a Micro-F1 of 0.8882 with reduced-dimensional features, recovering 88.69% of the performance gap to fully supervised learning. Additional experiments on the RCV1 Topics and AAPD datasets confirm that the advantage of Correct→Clean is consistently reproduced across domains and dataset sizes. These findings demonstrate that coupling LLM-generated annotations with ordered label correction offers an effective means of addressing the joint effects of missing and noisy labels, providing a promising approach to financial text classification when expert annotations are scarce.