arXiv:2511. 05385v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) utilizes external knowledge to augment Large Language Models' (LLMs) reliability.
By Chao Zhang, Yuhao Wang, Derong Xu, Haoxin Zhang, Yuanjie Lyu, Yuhao Chen, Shuochen Liu, Tong Xu, Xiangyu Zhao, Yan Gao, Yao Hu, Enhong Chen
arXiv:2606. 30133v1 Announce Type: cross Abstract: Retrieval-augmented generation built on knowledge graphs (Graph RAG) outperforms flat passage retrieval on multi-hop question answering by leveraging graph structure.
By Illia Makarov, Mykola Glybovets
arXiv:2608.12129v2 Announce Type: replace
Abstract: While retrieval-augmented generation (RAG) has proven effective at giving LLMs access to external knowledge, mainstream dense-retrieval implementat...
By Yuchao Wu, Junqin Li, XingCheng Liang, Yongjie Chen, Yinghao Liang, Linyuan Mo, Guanxian Li
SAG (SQL‑Retrieval Augmented Generation) is a structured retrieval framework that indexes documents as event‑entity pairs, forming latent hyperedges that preserve n‑ary relations without building a global knowledge graph. At query time, shared entities act as join keys, dynamically creating a query‑scoped neighborhood of related events while keeping each evidence chunk intact. Experiments on HotpotQA, 2WikiMultiHopQA, and MuSiQue demonstrate that SAG outperforms existing dense‑retrieval baselines, achieving the highest recall and end‑to‑end QA performance, especially as reasoning‑chain complexity grows.
By Yuchao Wu, Junqin Li, Xingcheng Liang, Yongjie Chen, Yinghao Liang, Linyuan Mo, Guanxian Li
arXiv:2606. 00610v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) has become an essential method for mitigating hallucinations in Large Language Models (LLMs) by leveraging external knowledge.
By Chuanjie Wu, Zhishang Xiang, Yunbo Tang, Zerui Chen, Qinggang Zhang, Jinsong Su
arXiv:2606. 05658v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by grounding their responses in external knowledge, but conventional pipelines rely on static, single-step retrieval that limits performance on complex queries.
By Anuj Maharjan, Devinder Kaur, Richard Molyet