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Shapley values enhance LLM explainability in finance

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method using Shapley values to explain the behavior of large language models (LLMs) in financial applications. This approach aims to align LLM explanations with established financial domain knowledge, addressing the critical need for explainability in the high-stakes finance industry. Empirical evaluations suggest that Shapley-based attributions can provide meaningful insights consistent with financial reasoning. AI

IMPACT Enhances trust and adoption of LLMs in finance by providing domain-specific explainability.

RANK_REASON The item is an academic paper detailing a new methodology for explaining machine learning models in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Shapley values enhance LLM explainability in finance

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The item is an academic paper detailing a new methodology for explaining machine learning models in a specific domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Dangxing Chen, Pengzhan Guo ·

    Shapley in Context: Explaining Financial Language with Domain Expertise

    arXiv:2607.00856v1 Announce Type: cross 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 …

  2. arXiv cs.LG TIER_1 English(EN) · Pengzhan Guo ·

    Shapley in Context: Explaining Financial Language with Domain Expertise

    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 numer…

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