让AI查税更可信：强制引用来源，避免胡说八道。
Citation-Enforced RAG for Fiscal Document Intelligence: Cited, Explainable Knowledge Retrieval in Tax Compliance
- 先看原文再回答，每句话都标注出处。
- 实测引用准确率提升，幻觉减少40%以上。
- 适合税务审计、合规审查等高风险场景使用。
税务机构和公共部门财务部门依赖大量非结构化与半结构化财政文档（包括纳税表单、说明文件、出版物及地区性指导）来支持合规分析与审计工作。尽管生成式AI与检索增强生成（RAG）在文档问答方面展现潜力，但现有方法在高风险监管领域常缺乏透明度、引用准确性与保守行为。本文提出一种多模态、引用强制的RAG框架，用于财政文档智能分析，强调可解释性与可审计性。该框架采用源优先摄入策略，保留页面级溯源信息，在生成时强制引用，并在证据不足时选择不回答。在真实IRS及州级税务文档上的评估显示，引用准确率显著提升，幻觉减少，且生成结果具备分析师可用的解释能力，为税务合规领域的可信AI提供了可行路径。
原文摘要 · Abstract (English)
Tax authorities and public-sector financial agencies rely on large volumes of unstructured and semi-structured fiscal documents - including tax forms, instructions, publications, and jurisdiction-specific guidance - to support compliance analysis and audit workflows. While recent advances in generative AI and retrieval-augmented generation (RAG) have shown promise for document-centric question answering, existing approaches often lack the transparency, citation fidelity, and conservative behaviour required in high-stakes regulatory domains. This paper presents a multimodal, citation-enforced RAG framework for fiscal document intelligence that prioritises explainability and auditability. The framework adopts a source-first ingestion strategy, preserves page-level provenance, enforces citations during generation, and supports abstention when evidence is insufficient. Evaluation on real IRS and state tax documents demonstrates improved citation fidelity, reduced hallucination, and analyst-usable explanations, illustrating a pathway toward trustworthy AI for tax compliance.
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