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Retrieval-augmented generation (RAG) agents are increasingly deployed to answer questions over local knowledge bases that cannot be centralized due to knowledge-sovereignty constraints.
Simple bm25 extension to multiple weighted fields
Stephen Robertson, Hugo Zaragoza, and Michael Taylor · 2004
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Cluster analysis
Brian S Everitt, Sabine Landau, Morven Leese, and Daniel Stahl · 2011
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Algorithms for hierarchical clustering: an overview
Fionn Murtagh and Pedro Contreras · 2012
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Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes · 2017
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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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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al · 2019
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Comparative study of single linkage, complete linkage, and ward method of agglomerative clustering
Shweta Sharma, Neha Batra, et al · 2019
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Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang · 2020
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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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Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Edouard Grave · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 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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Unsupervised dense information retrieval with contrastive learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave · 2021
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Language-agnostic topic classification for wikipedia
Isaac Johnson, Martin Gerlach, and Diego Sáez-Trumper · 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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LlamaIndex, 11 2022
Jerry Liu · 2022
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Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi · 2022
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Measuring and narrowing the compositionality gap in language models
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis · 2022
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Retrieval-augmented generation across heterogeneous knowledge
Wenhao Yu · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al · 2023
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Metagpt: Meta programming for multi-agent collaborative framework
Sirui Hong, Xiawu Zheng, Jonathan Chen, Yuheng Cheng, Jinlin Wang, Ceyao Zhang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, et al · 2023
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Active retrieval augmented generation
Zhengbao Jiang, Frank F Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig · 2023
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Efficient memory management for large language model serving with pagedattention
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica · 2023
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Routing to the expert: Efficient reward-guided ensemble of large language models
Keming Lu, Hongyi Yuan, Runji Lin, Junyang Lin, Zheng Yuan, Chang Zhou, and Jingren Zhou · 2023
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Query rewriting for retrieval-augmented large language models
Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan · 2023
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Enhancing retrieval-augmented large language models with iterative retrieval-generation synergy
Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen · 2023
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Plan × \times rag: Planning-guided retrieval augmented generation
Prakhar Verma, Sukruta Prakash Midigeshi, Gaurav Sinha, Arno Solin, Nagarajan Natarajan, and Amit Sharma · 2024
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Benchmarking retrieval-augmented generation for medicine
Guangzhi Xiong, Qiao Jin, Zhiyong Lu, and Aidong Zhang · 2024
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Corrective retrieval augmented generation
Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling · 2024
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Rankrag: Unifying context ranking with retrieval-augmented generation in llms
Yue Yu, Wei Ping, Zihan Liu, Boxin Wang, Jiaxuan You, Chao Zhang, Mohammad Shoeybi, and Bryan Catanzaro · 2024
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Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Rich James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih · 2023
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Autogen: Enabling next-gen llm applications via multi-agent conversation framework
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang · 2023
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Retrieval meets long context large language models
Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shoeybi, and Bryan Catanzaro · 2023
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Competeai: Understanding the competition behaviors in large language model-based agents
Qinlin Zhao, Jindong Wang, Yixuan Zhang, Yiqiao Jin, Kaijie Zhu, Hao Chen, and Xing Xie · 2023
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C-fedrag: A confidential federated retrieval-augmented generation system
Parker Addison, Minh-Tuan H Nguyen, Tomislav Medan, Mohammad T Manzari, Brendan McElrone, Laksh Lalwani, Aboli More, Smita Sharma, Holger R Roth, Isaac Yang, et al · 2024
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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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Improving factuality and reasoning in language models through multiagent debate
Yilun Du, Shuang Li, Antonio Torralba, Joshua B Tenenbaum, and Igor Mordatch · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, et al · 2024
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Dongfang Zhao · 2024
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Efficientrag: Efficient retriever for multi-hop question answering
Ziyuan Zhuang, Zhiyang Zhang, Sitao Cheng, Fangkai Yang, Jia Liu, Shujian Huang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, and Qi Zhang · 2024
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Federated learning and rag integration: a scalable approach for medical large language models
Jincheol Jung, Hongju Jeong, and Eui-Nam Huh · 2025
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Towards agentic rag with deep reasoning: A survey of rag-reasoning systems in llms
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Agentic RAG: turbocharge your RAG with query reformulation and self-query
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Privacy-enhancing paradigms within federated multi-agent systems
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Agentic retrieval-augmented generation: A survey on agentic rag
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Learning to route llms from bandit feedback: One policy, many trade-offs
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Masrouter: Learning to route llms for multi-agent systems
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The vision wormhole: Latent-space communication in heterogeneous multi-agent systems
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Icl-router: In-context learned model representations for llm routing
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Skillrouter: Retrieve-and-rerank skill selection for llm agents at scale
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