Shapley in Context: Explaining Financial Language with Domain Expertise
For the finance domain, where explainability is critical due to high stakes and regulations, this work shows that general-purpose Shapley values can produce explanations aligned with domain expertise, though the approach is incremental.
This work studies whether Shapley-based attributions for large language models in financial text applications align with established financial domain knowledge, demonstrating through theoretical analysis and empirical evaluations that they can yield consistent explanations.
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. In this work, we study the explainability of financial textual data modeled by large language models through the lens of the Shapley value. Specifically, we investigate whether Shapley based attributions align with established financial domain knowledge. Through rigorous theoretical analysis and extensive empirical evaluations, we demonstrate that Shapley values can yield explanations that are consistent with financial reasoning and can offer meaningful insights into the model's behavior in text based financial applications.