用符号规则+检索提升医保政策审查效率与可解释性
From Policy to Logic for Efficient and Interpretable Coverage Assessment
- 结合检索与符号推理，自动提取政策关键条款
- 推理成本降低44%，F1分数提升4.5%
- 适合医疗政策审核等需高可信场景
大型语言模型在解读复杂法律和政策文本方面展现出强大能力，但在主观性较强的文档分析中易出现幻觉与不一致，尤其在医疗覆盖政策审查中影响可靠性。本文提出一种辅助人工审查的方法，通过引入关注覆盖范围的检索器与符号规则推理相结合，精准定位相关政策语句，将其组织为明确的事实与规则，并生成可审计的推理依据。该混合系统显著减少了LLM推理次数，整体模型成本降低44%，同时在F1评分上提升4.5%，验证了方法在效率与有效性上的双重优势。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have demonstrated strong capabilities in interpreting lengthy, complex legal and policy language. However, their reliability can be undermined by hallucinations and inconsistencies, particularly when analyzing subjective and nuanced documents. These challenges are especially critical in medical coverage policy review, where human experts must be able to rely on accurate information. In this paper, we present an approach designed to support human reviewers by making policy interpretation more efficient and interpretable. We introduce a methodology that pairs a coverage-aware retriever with symbolic rule-based reasoning to surface relevant policy language, organize it into explicit facts and rules, and generate auditable rationales. This hybrid system minimizes the number of LLM inferences required which reduces overall model cost. Notably, our approach achieves a 44% reduction in inference cost alongside a 4.5% improvement in F1 score, demonstrating both efficiency and effectiveness.
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