Problem
Research questions and friction points this paper is trying to address.
Retrieval-augmented generation
Document reliability
Trust propagation
Feedback-Assisted Trust Propagation over Document Relation Graphs for Retrieval-Augmented Generation
🤖 AI Summary
为解决检索增强生成系统中的文档可靠性问题，本文提出TrustPropRAG方法，通过构建文档关系图并利用少量人工反馈进行信任信号的多跳传播来提高文档选择和答案生成的质量。
📝 Abstract
Retrieval-augmented generation (RAG) systems rely on external corpora that may contain outdated, contradictory, noisy, or unreliable documents, introducing reliability risks.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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Trust Propagation
Document Relation Graphs
Retrieval-Augmented Generation
Human Feedback
Reliability Signals
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