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Retrieval-Augmented Generation (RAG) enables large language models to provide more precise and pertinent responses by incorporating external knowledge.
Duc 2005: Evaluation of question-focused summarization systems
Hoa Trang Dang · 2006
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Graph-based knowledge representation: computational foundations of conceptual graphs
Michel Chein and Marie-Laure Mugnier · 2008
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Graph databases: new opportunities for connected data
Ian Robinson, Jim Webber, and Emil Eifrem · 2015
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Recent advances in document summarization
Jin-ge Yao, Xiaojun Wan, and Jianguo Xiao · 2017
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Tal Baumel, Matan Eyal, and Michael Elhadad · 2018
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Query focused abstractive summarization via incorporating query relevance and transfer learning with transformer models
Md Tahmid Rahman Laskar, Enamul Hoque, and Jimmy Huang · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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Retrieval augmentation reduces hallucination in conversation
Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston · 2021
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Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al · 2022
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Time-aware language models as temporal knowledge bases
Bhuwan Dhingra, Jeremy R Cole, Julian Martin Eisenschlos, Daniel Gillick, Jacob Eisenstein, and William W Cohen · 2022
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Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc Le, et al · 2022
Cited alongside, same era.
Retrieval-based language models and applications
Akari Asai, Sewon Min, Zexuan Zhong, and Danqi Chen · 2023
Cited alongside, same era.
Self-rag: Learning to retrieve, generate, and critique through self-reflection
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi · 2023
Cited alongside, same era.
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
Cited alongside, same era.
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
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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
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Lightrag: Simple and fast retrieval-augmented generation
Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, and Chao Huang · 2024
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Realtime qa: what’s the answer right now?
Jungo Kasai, Keisuke Sakaguchi, Ronan Le Bras, Akari Asai, Xinyan Yu, Dragomir Radev, Noah A Smith, Yejin Choi, Kentaro Inui, et al · 2024
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Xianzhi Li, Samuel Chan, Xiaodan Zhu, Yulong Pei, Zhiqiang Ma, Xiaomo Liu, and Sameena Shah · 2023
Cited alongside, same era.
Ra-dit: Retrieval-augmented dual instruction tuning
Xi Victoria Lin, Xilun Chen, Mingda Chen, Weijia Shi, Maria Lomeli, Rich James, Pedro Rodriguez, Jacob Kahn, Gergely Szilvasy, Mike Lewis, et al · 2023
Cited alongside, same era.
Search augmented instruction learning
Hongyin Luo, Tianhua Zhang, Yung-Sung Chuang, Yuan Gong, Yoon Kim, Xixin Wu, Helen Meng, and James Glass · 2023
Cited alongside, same era.
In-context retrieval-augmented language models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham · 2023
Cited alongside, same era.
Reducing hallucination in structured outputs via retrieval-augmented generation
Patrice Béchard and Orlando Marquez Ayala · 2024
Cited alongside, same era.
Jiarui Li, Ye Yuan, and Zehua Zhang · 2024
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Mufei Li, Siqi Miao, and Pan Li · 2024
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Memorag: Moving towards next-gen rag via memory-inspired knowledge discovery
Hongjin Qian, Peitian Zhang, Zheng Liu, Kelong Mao, and Zhicheng Dou · 2024
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Multihop-rag: Benchmarking retrieval-augmented generation for multi-hop queries
Yixuan Tang and Yi Yang · 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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