Dangxing Chen

Abstract In recent years, large language models have achieved remarkable success and have seen growing adoption in financial applications. At the same time, explainability remains critical in finance, a domain characterized by high stakes and strict regulatory requirements. Although numerous methods have been proposed to explain black-box machine learning models, the majority of these approaches are designed for general-purpose tasks and do not incorporate domain-specific knowledge.