The paper introduces TrustPropRAG, a method that builds a document relation graph to propagate reliability signals across documents in retrieval‑augmented generation systems. By anchoring this propagation with a limited set of human feedback on document reliability, the approach estimates trust scores through an optimization problem that incorporates pairwise relations and user input. These trust scores are then used to select more reliable documents and generate answers that are aware of document trustworthiness, leading to improved retrieval quality and exact match performance compared to baselines, even with sparse or noisy feedback.
By Zhuoheng Li, Ying Chen
arXiv:2609.07075v1 Announce Type: new
Abstract: Retrieval-augmented generation (RAG) is commonly evaluated by whether the final answer is correct. That test is insufficient: an answer can match its r...
By Ramon Gonzalez, Antonio Diaz
ReliableRAG is a new framework for Retrieval-Augmented Generation that tackles misinformation in multi‑hop question answering. It extracts structured triples from retrieved documents, evaluates each triple’s reliability by combining semantic relevance to the query with credibility, and keeps only the top‑K reliable, non‑redundant triples. Using these refined triples, the system builds robust reasoning chains that filter out deceptive misinformation and produce accurate, trustworthy answers.
By Jinpu Jiang, Xuan Wu, Wenhao Song, Bo Yang, You Zhou, Hongwei Ge, Heow Pueh Lee, Yanchun Liang, Chunguo Wu
arXiv:2607. 22584v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation pipelines rank retrieved documents by semantic similarity alone, without accounting for source provenance or credibility.
By Yuktha Tata Koganti, Hugo Garrido-Lestache Belinchon
arXiv:2605. 17301v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in practice.
By Chenyu Wang, Yueyuan Li, Yingmin Liu, Yang Shu
arXiv:2608. 16515v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) improves large language models by grounding generation in external evidence, but it also introduces a source trust problem: retrieved context may be useful, irrelevant, or even misleading.
By Haolin Jin, Pengyue Yang, Huaming Chen
arXiv:2606. 05901v1 Announce Type: cross Abstract: Large language models (LLMs) have fundamentally transformed the landscape of Natural Language Processing.
By Christopher J. Wedge, Joshua Stutter, Danny Dixon, Jacek Ca{\l}a
arXiv:2609.24028v1 Announce Type: new
Abstract: Generating coherent meta-reviews from multiple peer reviews is challenging when reviewer evidence conflicts and varies in reliability. Existing approac...
By Xinzhe Wang, Fei Tao, Jiang Xie, Hong Yu, Ye Wang
arXiv:2609.22805v1 Announce Type: new
Abstract: Natural language generation (NLG) tasks span the spectrum of conditional entropy, ranging from highly constrained machine translation to open-ended dia...
By Maitreya Prafulla Chitale, Ketaki Mangesh Shetye, Yash More, Harshit Gupta, Manav Chaudhary, Manish Shrivastava, Vasudeva Varma
arXiv:2606. 18075v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has emerged as a paradigm for enhancing large language models (LLMs) with external knowledge, yet existing graph-based methods face a fundamental limitation: entity-centric and chunk-centric approaches operate on representations anchored to original text without true knowledge fusion.
By Haoyang Zhong, Yifei Sun, Antong Zhang, Chunping Wang, Lei Chen, Yang Yang
W-RAG is a source-aware retrieval framework designed for enterprise document generation from heterogeneous knowledge bases. It uses ontology-guided retrieval, local ranking within each knowledge base, and source-level weighting to balance evidence from diverse sources. A new dataset covering multiple document types and industry domains demonstrates that W-RAG improves document coverage and generation quality compared to standard RAG pipelines.
By Hridya Dhulipala, Rajesh Ombase, Michael Wang, Tien N. Nguyen
arXiv:2609.22537v1 Announce Type: new
Abstract: Enterprise AI assistants must produce responses that are verifiable and traceable to source evidence. However, retrieval augmented generation (RAG) ove...
By Anubha Kabra, Katie Jooyoung Kim, Colin Zhiwei Kou, Helene Sajer, Yimei Fan, Radomir Cisar, Heather Greenhalgh, Gabriel Martinez Vidiri