发表机构
College of Information Sciences and Technology, The Pennsylvania State University（宾夕法尼亚州立大学信息科学与技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对RAG系统的文档可靠性风险，提出TrustPropRAG方法，通过文档关系图与反馈辅助的多跳信任传播提升检索质量与答案可靠性，且在稀疏噪声反馈下表现稳健。
AI 中文摘要
检索增强生成（RAG）系统依赖的外部语料库可能包含过时、矛盾、有噪声或不可靠的文档，带来可靠性风险。现有工作已利用文档关系提升RAG的答案可靠性，但仅能在直接对比的文档对间传递可靠性信号。为此，本文提出TrustPropRAG，将文档关系构建为图结构，通过图上的多跳传播估计文档可靠性。TrustPropRAG以有限的人类反馈（收集成本高）作为传播锚点，将基于反馈的可靠性信号扩展至整个语料库。具体而言，该方法基于构建的文档关系图，通过建立并求解优化问题，结合文档间的成对关系与用户反馈，为每个文档估计信任分数。这些分数被用于优化可靠文档的选择，支持感知信任的答案生成。实验结果表明，TrustPropRAG在检索质量和精确匹配指标上均优于基线方法，且在稀疏和有噪声的反馈下仍保持鲁棒性。
英文摘要
Retrieval-augmented generation (RAG) systems rely on external corpora that may contain outdated, contradictory, noisy, or unreliable documents, introducing reliability risks. Prior work has leveraged document relations to improve the answer reliability of RAG. To propagate reliability signals beyond directly compared document pairs, we propose TrustPropRAG, which structures document relations as a graph and estimates document reliability through multi-hop propagation across the graph. TrustPropRAG anchors this propagation with a limited set of human feedback on document reliability, extending these costly-to-collect feedback-based reliability signals across the whole corpus. Specifically, based on the constructed document relation graph, TrustPropRAG estimates a trust score for each document by formulating and solving an optimization problem that jointly captures pairwise document relations and user feedback. These scores are then used to improve the selection of reliable documents and support trust-aware answer generation. Evaluation results show that TrustPropRAG improves both retrieval quality and exact match over baselines, and remains robust under sparse and noisy feedback.
CommentsEMNLP 2026 Findings
