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A Controlled LLM Workflow for Evidence-Grounded Diagnosis of Risk Score Differences

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arXiv:2610.23962v1 Announce Type: new Abstract: We propose a controlled Large Language Model (LLM) workflow for explaining risk score differences using similar historical transactions. The workflow combines reference retrieval and contrastive SHapley Additive exPlanations (SHAP) evidence with reference-based LLM reasoning and deterministic validation. Experiments on a test dataset show that the workflow produces more consistent, evidence-grounded and traceable diagnoses than the same LLM without structured workflow guidance.

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采集于 2026/10/20 21:06:32(北京时间)。 arXiv 日期为本次公告时间,论文初次投稿日期请查看原文版本记录。

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