Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial
  Narratives
Jiaxin Liu Yi Yang Kar Yan Tam
In this paper, we introduce the Financial-STS task, a financial
domain-specific NLP task designed to measure the nuanced semantic similarity
between pairs of financial narratives. These narratives originate from the
financial statements of the same company but correspond to different periods,
such as year-over-year comparisons. Measuring the subtle semantic differences
between these paired narratives enables market stakeholders to gauge changes
over time in the company's financial and operational situations, which is
critical for financial decision-making. We find that existing pretrained
embedding models and LLM embeddings fall short in discerning these subtle
financial narrative shifts. To address this gap, we propose an LLM-augmented
pipeline specifically designed for the Financial-STS task. Evaluation on a
human-annotated dataset demonstrates that our proposed method outperforms
existing methods trained on classic STS tasks and generic LLM embeddings.