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Retrieval-augmented Generation (RAG) relies on effective retrieval capabilities, yet traditional sparse and dense retrievers inherently struggle with multi-hop retrieval scenarios.
Reciprocal rank fusion outperforms condorcet and individual rank learning methods
Gordon V. Cormack, Charles L A Clarke, and Stefan Buettcher. 2009 · 2009
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Entity disambiguation for knowledge base population
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Search result diversification
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Learning entity and relation embeddings for knowledge graph completion
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015 · 2015
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Oscillations and episodic memory: Addressing the synchronization/desynchronization conundrum
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Diverse beam search: Decoding diverse solutions from neural sequence models
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
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Directional coupling of slow and fast hippocampal gamma with neocortical alpha/beta oscillations in human episodic memory
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Comparison of diverse decoding methods from conditional language models
Daphne Ippolito, Reno Kriz, João Sedoc, Maria Kustikova, and Chris Callison-Burch. 2019 · 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 · 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, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
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REBEL: Relation extraction by end-to-end language generation
Pere-Lluís Huguet Cabot and Roberto Navigli. 2021 · 2021
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Entailment as few-shot learner
Sinong Wang, Han Fang, Madian Khabsa, Hanzi Mao, and Hao Ma. 2021 · 2021
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MuSiQue: Multihop questions via single-hop question composition
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022 · 2022
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Howard Chen, Ramakanth Pasunuru, Jason Weston, and Asli Celikyilmaz. 2023 · 2023
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Active retrieval augmented generation
Zhengbao Jiang, Frank Xu, Luyu Gao, Zhiqing Sun, Qian Liu, Jane Dwivedi-Yu, Yiming Yang, Jamie Callan, and Graham Neubig. 2023 · 2023
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TRACE the evidence: Constructing knowledge-grounded reasoning chains for retrieval-augmented generation
Jinyuan Fang, Zaiqiao Meng, and Craig MacDonald. 2024 · 2024
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HippoRAG: Neurobiologically inspired long-term memory for large language models
Bernal Jimenez Gutierrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. 2024 · 2024
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GraphReader: Building graph-based agent to enhance long-context abilities of large language models
Shilong Li, Yancheng He, Hangyu Guo, Xingyuan Bu, Ge Bai, Jie Liu, Jiaheng Liu, Xingwei Qu, Yangguang Li, Wanli Ouyang, Wenbo Su, and Bo Zheng. 2024 · 2024
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KAG: Boosting llms in professional domains via knowledge augmented generation
Lei Liang, Mengshu Sun, Zhengke Gui, Zhongshu Zhu, Zhouyu Jiang, Ling Zhong, Yuan Qu, Peilong Zhao, Zhongpu Bo, Jin Yang, Huaidong Xiong, Lin Yuan, Jun Xu, Zaoyang Wang, Zhiqiang Zhang, Wen Zhang, Huajun Chen, Wenguang Chen, and Jun Zhou. 2024 · 2024
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Graph elicitation for guiding multi-step reasoning in large language models
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Graph-guided reasoning for multi-hop question answering in large language models
Jinyoung Park, Ameen Patel, Omar Zia Khan, Hyunwoo J. Kim, and Joo-Kyung Kim. 2023 · 2023
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Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2023 · 2023
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Query2doc: Query expansion with large language models
Liang Wang, Nan Yang, and Furu Wei. 2023 · 2023
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LongRoPE: Extending LLM context window beyond 2 million tokens
Yiran Ding, Li Lyna Zhang, Chengruidong Zhang, Yuanyuan Xu, Ning Shang, Jiahang Xu, Fan Yang, and Mao Yang. 2024 · 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 · 2024
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Jinyoung Park, Ameen Patel, Omar Zia Khan, Hyunwoo J. Kim, and Joo-Kyung Kim. 2024 · 2024
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RAPTOR: Recursive abstractive processing for tree-organized retrieval
Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D Manning. 2024 · 2024
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Retrieval-augmented retrieval: Large language models are strong zero-shot retriever
Tao Shen, Guodong Long, Xiubo Geng, Chongyang Tao, Yibin Lei, Tianyi Zhou, Michael Blumenstein, and Daxin Jiang. 2024a · 2024
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Improving retrieval-augmented text-to-SQL with AST-based ranking and schema pruning
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DRAGIN: Dynamic retrieval augmented generation based on the real-time information needs of large language models
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Knowledge graph prompting for multi-document question answering
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