用博弈论方法解释金融文本模型，结果符合专业判断。
Shapley in Context: Explaining Financial Language with Domain Expertise
- 基于谢林值分析金融文本中关键词贡献度
- 实证显示解释结果与金融领域知识一致
- 适合关注模型可解释性的金融从业者
近年来，大语言模型在金融应用中取得显著进展，但可解释性仍是关键挑战。尽管已有众多解释黑箱模型的方法，但多数适用于通用任务，缺乏领域知识融合。本文从谢林值视角研究大语言模型对金融文本的可解释性，通过严谨的理论分析与广泛实证评估，证明谢林值能生成与金融推理一致的解释，为基于文本的金融应用提供有意义的模型行为洞察。
原文摘要 · Abstract (English)
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.
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