Researchers propose controlled LLM workflow for evidence‑grounded risk score diagnosis
A new arXiv paper introduces a controlled workflow that guides large language models (LLMs) to explain differences in risk scores by referencing similar past transactions. The method links three components: a retrieval step that pulls relevant historical records, contrastive SHapley Additive exPlanations (SHAP) that highlight evidence, and a reference‑based LLM reasoning stage that produces a…
Key points
- Workflow combines reference retrieval, contrastive SHAP evidence, and reference‑based LLM reasoning
- Deterministic validation added to enforce consistency and traceability
- Experiments show higher evidence grounding and consistency versus unguided LLM output
The authors evaluate the workflow on a test dataset and report that it yields diagnoses that are more consistent, better grounded in evidence, and more traceable than those generated by the same LLM when it operates without the structured guidance. The paper positions the approach as a way to improve transparency and accountability in automated risk‑scoring systems, which are widely used in finance and other regulated domains.
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