引用强化的财政文档智能：在税务合规中的引用、可解释知识检索
Citation-Enforced RAG for Fiscal Document Intelligence: Cited, Explainable Knowledge Retrieval in Tax Compliance
AI总结：
本文提出一种引用强化的RAG框架，用于财政文档智能，强调可解释性和可审计性，通过源优先的摄入策略和引用强制机制提升税务合规中的引用准确性与解释性。
摘要：
税务机构和公共部门财务机构依赖大量非结构化和半结构化的财政文档，包括税表、说明、出版物和地方特定指南，以支持合规分析和审计流程。尽管生成式AI和检索增强生成（RAG）的最新进展在文档导向的问题回答中展现出潜力，但现有方法往往缺乏高风险监管领域所需的透明度、引用保真度和保守行为。本文提出了一种多模态、引用强化的RAG框架，用于财政文档智能，优先考虑可解释性和可审计性。该框架采用源优先的摄入策略，保留页面级的来源信息，在生成过程中强制引用，并在证据不足时支持回避。在真实IRS和州税文档上的评估显示，引用保真度提高，幻觉减少，且提供可被分析人员使用的解释，展示了通往可信AI用于税务合规的道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
