arXiv:2609.10239v1 Announce Type: cross
Abstract: Graph-based retrieval can improve multi-hop question answering, but existing approaches often incur high query-time costs and produce diffuse, oversi...
By Daniel Alejandro Coll Tejeda, Pedro Garc\'ia L\'opez, Daniel Barcelona-Pons
arXiv:2504. 20114v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) systems face significant challenges in multi-hop question answering (MHQA), where complex queries require synthesizing information across multiple document chunks.
By Zhonghao Li, Kunpeng Zhang, Jinghuai Ou, Shuliang Liu, Xuming Hu
arXiv:2603. 29875v3 Announce Type: replace-cross Abstract: One of the key problems in Retrieval-augmented generation (RAG) systems is that chunk-based retrieval pipelines represent the source chunks as atomic objects, mixing the information contained within such a chunk into a single vector.
By Ryszard Tuora, Mateusz Gali\'nski, Micha{\l} Godziszewski, Micha{\l} Karpowicz, Mateusz Czy\.znikiewicz, Adam Kozakiewicz, Tomasz Zi\k{e}tkiewicz
arXiv:2608. 04444v1 Announce Type: cross Abstract: Large language models (LLMs) often generate inaccurate answers due to their reliance on static internal knowledge.
By Jiaoyang Li, Junhao Ruan, Shengwei Tang, Kaiyan Chang, Zhengtao Yu, Tong Xiao, Jingbo Zhu
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
SelfGraphRAG is a framework that generates synthetic question‑answer pairs directly from the structure of a knowledge graph to train a query‑conditioned graph retriever. By capturing multi‑hop paths and local neighborhoods, the generated questions provide relational supervision without requiring manually labeled data. Experiments on multi‑hop question answering and classification tasks show that SelfGraphRAG improves retrieval precision and downstream reasoning performance compared to embedding‑based baselines.
By Ben Lagnese, Manas Gaur