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Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs.
Precise zero-shot dense retrieval without relevance labels
Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan · 2022
Earlier work this paper cites.
Recipe for a general, powerful, scalable graph transformer
Ladislav Rampášek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
Earlier work this paper cites.
Congrat: Self-supervised contrastive pretraining for joint graph and text embeddings
William Brannon, Suyash Fulay, Hang Jiang, Wonjune Kang, Brandon Roy, Jad Kabbara, and Deb Roy · 2023
Earlier work this paper cites.
Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang · 2023
Earlier work this paper cites.
Grenade: Graph-centric language model for self-supervised representation learning on text-attributed graphs
Yichuan Li, Kaize Ding, and Kyumin Lee · 2023
Earlier work this paper cites.
In-context retrieval-augmented language models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham · 2023
Earlier work this paper cites.
Graph-aware language model pre-training on a large graph corpus can help multiple graph applications
Han Xie, Da Zheng, Jun Ma, Houyu Zhang, Vassilis N Ioannidis, Xiang Song, Qing Ping, Sheng Wang, Carl Yang, Yi Xu, et al · 2023
Earlier work this paper cites.
Rq-rag: Learning to refine queries for retrieval augmented generation
Chi-Min Chan, Chunpu Xu, Ruibin Yuan, Hongyin Luo, Wei Xue, Yike Guo, and Jie Fu · 2024
Cited alongside, same era.
Llaga: Large language and graph assistant
Runjin Chen, Tong Zhao, AJAY KUMAR JAISWAL, Neil Shah, and Zhangyang Wang · 2024
Cited alongside, same era.
From local to global: A graph rag approach to query-focused summarization
Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson · 2024
Cited alongside, same era.
Ragas: Automated evaluation of retrieval augmented generation
Shahul Es, Jithin James, Luis Espinosa Anke, and Steven Schockaert · 2024
Cited alongside, same era.
A survey on rag meeting llms: Towards retrieval-augmented large language models
Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, and Qing Li · 2024
Cited alongside, same era.
Memorag: Moving towards next-gen rag via memory-inspired knowledge discovery, 2024
Hongjin Qian, Peitian Zhang, Zheng Liu, Kelong Mao, and Zhicheng Dou · 2024
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Evaluating retrieval quality in retrieval-augmented generation
Alireza Salemi and Hamed Zamani · 2024
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Rag-ex: A generic framework for explaining retrieval augmented generation
Viju Sudhi, Sinchana Ramakanth Bhat, Max Rudat, and Roman Teucher · 2024
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Graphgpt: Graph instruction tuning for large language models
Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Lixin Su, Suqi Cheng, Dawei Yin, and Chao Huang · 2024
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R-eval: A unified toolkit for evaluating domain knowledge of retrieval augmented large language models
Shangqing Tu, Yuanchun Wang, Jifan Yu, Yuyang Xie, Yaran Shi, Xiaozhi Wang, Jing Zhang, Lei Hou, and Juanzi Li · 2024
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One for all: Towards training one graph model for all classification tasks
Hao Liu, Jiarui Feng, Lecheng Kong, Ningyue Liang, Dacheng Tao, Yixin Chen, and Muhan Zhang · 2024
Cited alongside, same era.
Yuanjie Lyu, Zhiyu Li, Simin Niu, Feiyu Xiong, Bo Tang, Wenjin Wang, Hao Wu, Huanyong Liu, Tong Xu, and Enhong Chen · 2024
Cited alongside, same era.
Yue Yu, Wei Ping, Zihan Liu, Boxin Wang, Jiaxuan You, Chao Zhang, Mohammad Shoeybi, and Bryan Catanzaro · 2024
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Retrieval-augmented generation for ai-generated content: A survey
Penghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, and Bin Cui · 2024
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