Shapley values enhance LLM explainability in finance

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 →

• arXiv
• computational finance
• finance
• large language models
• machine learning
• mathematical finance
• Shapley value

• paper
• other

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