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Graph retrieval-augmented generation (GraphRAG) has effectively enhanced large language models in complex reasoning by organizing fragmented knowledge into explicitly structured graphs.
Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning · 2018
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From louvain to leiden: guaranteeing well-connected communities
Vincent A Traag, Ludo Waltman, and Nees Jan Van Eck · 2019
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Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa · 2020
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Qa-gnn: Reasoning with language models and knowledge graphs for question answering
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, and Jure Leskovec · 2021
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Musique: Multihop questions via single-hop question composition
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal · 2022
Earlier work this paper cites.
Hierarchy-aware multi-hop question answering over knowledge graphs
Junnan Dong, Qinggang Zhang, Xiao Huang, Keyu Duan, Qiaoyu Tan, and Zhimeng Jiang · 2023
Earlier work this paper cites.
Reasoning on graphs: Faithful and interpretable large language model reasoning
Linhao Luo, Yuan-Fang Li, Gholamreza Haffari, and Shirui Pan · 2023
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Unifying large language models and knowledge graphs: A roadmap
Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu · 2024
Earlier work this paper cites.
Knowledge graph prompting for multi-document question answering
Yu Wang, Nedim Lipka, Ryan A Rossi, Alexa Siu, Ruiyi Zhang, and Tyler Derr · 2024
Earlier work this paper cites.
Knowgpt: Knowledge graph based prompting for large language models
Qinggang Zhang, Junnan Dong, Hao Chen, Daochen Zha, Zailiang Yu, and Xiao Huang · 2024
Cited alongside, same era.
G-retriever: Retrieval-augmented generation for textual graph understanding and question answering
Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi · 2024
Cited alongside, same era.
Graph retrieval-augmented generation: A survey
Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, and Siliang Tang · 2024
Cited alongside, same era.
Retrieval-augmented generation with graphs (graphrag)
Haoyu Han, Yu Wang, Harry Shomer, Kai Guo, Jiayuan Ding, Yongjia Lei, Mahantesh Halappanavar, Ryan A Rossi, Subhabrata Mukherjee, Xianfeng Tang, et al · 2024
Cited alongside, same era.
From local to global: A graph rag approach to query-focused summarization
Graph-constrained reasoning: Faithful reasoning on knowledge graphs with large language models
Linhao Luo, Zicheng Zhao, Gholamreza Haffari, Yuan-Fang Li, Chen Gong, and Shirui Pan · 2024
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Yilin Xiao, Junnan Dong, Chuang Zhou, Su Dong, Qianwen Zhang, Di Yin, Xing Sun, and Xiao Huang · 2025
Closest in time.
Gfm-rag: graph foundation model for retrieval augmented generation
Linhao Luo, Zicheng Zhao, Gholamreza Haffari, Dinh Phung, Chen Gong, and Shirui Pan · 2025
Closest in time.
From rag to memory: Non-parametric continual learning for large language models
Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi, Sizhe Zhou, and Yu Su · 2025
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Eˆ 2graphrag: Streamlining graph-based rag for high efficiency and effectiveness
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Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson · 2024
Cited alongside, same era.
Lightrag: Simple and fast retrieval-augmented generation
Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, and Chao Huang · 2024
Cited alongside, same era.
Gnn-rag: Graph neural retrieval for large language model reasoning
Costas Mavromatis and George Karypis · 2024
Cited alongside, same era.
Hipporag: Neurobiologically inspired long-term memory for large language models
Bernal Jimenez Gutierrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su · 2024
Cited alongside, same era.
Raptor: Recursive abstractive processing for tree-organized retrieval
Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D Manning · 2024
Cited alongside, same era.
Modality-aware integration with large language models for knowledge-based visual question answering
Junnan Dong, Qinggang Zhang, Huachi Zhou, Daochen Zha, Pai Zheng, and Xiao Huang
Cited in the paper.
Multilingual large language model: A survey of resources, taxonomy and frontiers
Libo Qin, Qiguang Chen, Yuhang Zhou, Zhi Chen, Yinghui Li, Lizi Liao, Min Li, Wanxiang Che, and Philip S. Yu
Cited in the paper.
Clr-bench: Evaluating large language models in college-level reasoning
Junnan Dong, Zijin Hong, Yuanchen Bei, Feiran Huang, Xinrun Wang, and Xiao Huang
Cited in the paper.
Yibo Zhao, Jiapeng Zhu, Ye Guo, Kangkang He, and Xiang Li · 2025
Closest in time.
A survey of graph retrieval-augmented generation for customized large language models
Qinggang Zhang, Shengyuan Chen, Yuanchen Bei, Zheng Yuan, Huachi Zhou, Zijin Hong, Junnan Dong, Hao Chen, Yi Chang, and Xiao Huang · 2025
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Natural language understanding and inference with MLLM in visual question answering: A survey
Jiayi Kuang, Ying Shen, Jingyou Xie, Haohao Luo, Zhe Xu, Ronghao Li, Yinghui Li, Xianfeng Cheng, Xika Lin, and Yu Han · 2025
Closest in time.
Graphs meet ai agents: Taxonomy, progress, and future opportunities
Yuanchen Bei, Weizhi Zhang, Siwen Wang, Weizhi Chen, Sheng Zhou, Hao Chen, Yong Li, Jiajun Bu, Shirui Pan, Yizhou Yu, et al · 2025
Closest in time.