CiteGuard-RAG：一种以验证为中心的AI系统，用于基于证据的问答
CiteGuard-RAG: A Validation-Centered AI System for Evidence-Grounded Question Answering
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中文总结 AI 辅助
CiteGuard-RAG提出以验证为中心的RAG系统，集成混合检索、引用约束生成和句子级验证，在400题评估中实现99.1%检索准确率，证明显式验证对可信答案的关键作用。
中文摘要 AI 辅助
检索增强生成（RAG）可以改善对复杂信息的访问；然而，仅检索证据并不能确保答案有依据、引用有效或得到适当拒绝。本文介绍了CiteGuard-RAG，一个以验证为中心的AI系统，用于基于证据的问答。该系统集成了混合语义-词汇检索、引用约束生成、句子级依据验证和单次重新生成。在运行时使用验证来确定候选答案在最终交付前应被接受、拒绝还是重新生成。CiteGuard-RAG在受控住房法数据集、PrivacyQA和CUAD上的400个问题上进行了评估。在受控评估中，它实现了99.1%的检索准确率、98.3%的有依据答案准确率和98.3%的引用有效性，且没有验证检测到的幻觉。消融结果表明，当移除验证时，即使检索准确率保持不变，有依据答案准确率也会急剧下降。外部评估显示，虽然引用有效性仍然很强，但在领域转移下，证据利用、跨度对齐和弃权（不执行）校准变得更加困难。这些发现表明，可信赖的RAG系统需要在检索和最终答案交付之间进行显式验证。CiteGuard-RAG为在高风险信息访问中连接检索、生成、引用检查、弃权（不执行）和重新生成提供了一种实用架构。
英文摘要
Retrieval-augmented generation (RAG) can improve access to complex information; however, retrieving evidence alone does not ensure that answers are grounded, citation-valid, or appropriately refused. This paper introduces CiteGuard-RAG, a validation-centered AI system for evidence-grounded question answering. The system integrates hybrid semantic-lexical retrieval, citation-constrained generation, sentence-level grounding validation, and single-pass regeneration. Validation is used at runtime to determine whether a candidate answer should be accepted, refused, or regenerated before final delivery. CiteGuard-RAG is evaluated on 400 questions across a controlled housing-law dataset, PrivacyQA, and CUAD. In the controlled evaluation, it achieves 99.1% retrieval accuracy, 98.3% grounded-answer accuracy, and 98.3% citation validity, with no validation-detected hallucinations. Ablation results show that grounded-answer accuracy drops sharply when validation is removed, even when retrieval accuracy remains unchanged. External evaluation shows that while citation validity remains strong, evidence utilization, span alignment, and refusal calibration become harder under domain shift. These findings indicate that trustworthy RAG systems require explicit validation between retrieval and final answer delivery. CiteGuard-RAG provides a practical architecture for linking retrieval, generation, citation checking, abstention, and regeneration in high-stakes information access.
发表机构
- Western Michigan University（西密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。
