LabelFusion: Fusing Large Language Models with Transformer Encoders for Robust Financial News Classification

ArXi:2512.10793v2 Announce Type: replace-cross Financial news plays a central role in shaping investor sentiment and short-term dynamics in commodity markets. Many downstream financial applications, such as commodity price prediction or sentiment modeling, therefore rely on the ability to automatically identify news articles relevant to specific assets. However, obtaining large labeled corpora for financial text classification is costly, and transformer-based classifiers such as RoBERTa often degrade significantly in low-data regimes.