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Existing studies have optimized retrieval-augmented generation (RAG) across various sub-tasks, such as query understanding and retrieval refinement, but integrating these optimizations into a unified framework remains challenging.
Query reformulation for dynamic information integration
Yigal Arens, Craig A. Knoblock, and Wei-Min Shen. 1996 · 1996
Earlier work this paper cites.
Query expansion using local and global document analysis
Jinxi Xu and W. Bruce Croft. 1996 · 1996
Earlier work this paper cites.
A survey of automatic query expansion in information retrieval
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Earlier work this paper cites.
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 · 2018
Earlier work this paper cites.
Query expansion techniques for information retrieval: A survey
Hiteshwar Kumar Azad and Akshay Deepak. 2019 · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
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Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick S. H. 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
Earlier work this paper cites.
KILT: a benchmark for knowledge intensive language tasks
Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick S. H. Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktäschel, and Sebastian Riedel. 2021 · 2021
Earlier work this paper cites.
Musique: Multihop questions via single-hop question composition
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022 · 2022
Earlier work this paper cites.
Solving math word problems with process- and outcome-based feedback
Jonathan Uesato, Nate Kushman, Ramana Kumar, H. Francis Song, Noah Y. Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins. 2022 · 2022
Earlier work this paper cites.
Text embeddings by weakly-supervised contrastive pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022 · 2022
Earlier work this paper cites.
Llmlingua: Compressing prompts for accelerated inference of large language models
Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. 2023a · 2023
Cited alongside, same era.
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. 2023b · 2023
Cited alongside, same era.
Query rewriting in retrieval-augmented large language models
Xinbei Ma, Yeyun Gong, Pengcheng He, Hai Zhao, and Nan Duan. 2023 · 2023
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When not to trust language models: Investigating effectiveness of parametric and non-parametric memories
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Daniel Khashabi, and Hannaneh Hajishirzi. 2023 · 2023
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OpenAI. 2023 · 2023
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The llama 3 herd of models
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston Zhang, Aurélien Rodriguez, Austen Gregerson, Ava Spataru, Baptiste Rozière, Bethany Biron, Binh Tang, Bobbie Chern, Charlotte Caucheteux, Chaya Nayak, Chloe Bi, Chris Marra, Chris McConnell, Christian Keller, Christophe Touret, Chunyang Wu, Corinne Wong, Cristian Canton Ferrer, Cyrus Nikolaidis, Damien Allonsius, Daniel Song, Danielle Pintz, Danny Livshits, David Esiobu, Dhruv Choudhary, Dhruv Mahajan, Diego Garcia-Olano, Diego Perino, Dieuwke Hupkes, Egor Lakomkin, Ehab AlBadawy, Elina Lobanova, Emily Dinan, Eric Michael Smith, Filip Radenovic, Frank Zhang, Gabriel Synnaeve, Gabrielle Lee, Georgia Lewis Anderson, Graeme Nail, Grégoire Mialon, Guan Pang, Guillem Cucurell, Hailey Nguyen, Hannah Korevaar, Hu Xu, Hugo Touvron, Iliyan Zarov, Imanol Arrieta Ibarra, Isabel M. Kloumann, Ishan Misra, Ivan Evtimov, Jade Copet, Jaewon Lee, Jan Geffert, Jana Vranes, Jason Park, Jay Mahadeokar, Jeet Shah, Jelmer van der Linde, Jennifer Billock, Jenny Hong, Jenya Lee, Jeremy Fu, Jianfeng Chi, Jianyu Huang, Jiawen Liu, Jie Wang, Jiecao Yu, Joanna Bitton, Joe Spisak, Jongsoo Park, Joseph Rocca, Joshua Johnstun, Joshua Saxe, Junteng Jia, Kalyan Vasuden Alwala, Kartikeya Upasani, Kate Plawiak, Ke Li, Kenneth Heafield, Kevin Stone, and et al. 2024 · 2024
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Jinyuan Fang, Zaiqiao Meng, and Craig Macdonald. 2024 · 2024
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Adaptive-rag: Learning to adapt retrieval-augmented large language models through question complexity
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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. 2023 · 2023
Cited alongside, same era.
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 · 2023
Cited alongside, same era.
Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2023 · 2023
Cited alongside, same era.
Self-knowledge guided retrieval augmentation for large language models
Yile Wang, Peng Li, Maosong Sun, and Yang Liu. 2023 · 2023
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Self-rag: Learning to retrieve, generate, and critique through self-reflection
Akari Asai, Zeqiu Wu, Yizhong Wang, Avirup Sil, and Hannaneh Hajishirzi. 2024 · 2024
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 · 2024
Cited alongside, same era.
Mindsearch: Mimicking human minds elicits deep AI searcher
Zehui Chen, Kuikun Liu, Qiuchen Wang, Jiangning Liu, Wenwei Zhang, Kai Chen, and Feng Zhao. 2024 · 2024
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Soyeong Jeong, Jinheon Baek, Sukmin Cho, Sung Ju Hwang, and Jong Park. 2024 · 2024
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Sure: Summarizing retrievals using answer candidates for open-domain QA of llms
Jaehyung Kim, Jaehyun Nam, Sangwoo Mo, Jongjin Park, Sang-Woo Lee, Minjoon Seo, Jung-Woo Ha, and Jinwoo Shin. 2024b · 2024
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Rafe: Ranking feedback improves query rewriting for RAG
Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, and Ningyu Zhang. 2024 · 2024
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REPLUG: retrieval-augmented black-box language models
Weijia Shi, Sewon Min, Michihiro Yasunaga, Minjoon Seo, Richard James, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. 2024 · 2024
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Small models, big insights: Leveraging slim proxy models to decide when and what to retrieve for llms
Jiejun Tan, Zhicheng Dou, Yutao Zhu, Peidong Guo, Kun Fang, and Ji-Rong Wen. 2024 · 2024
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Planxrag: Planning-guided retrieval augmented generation
Prakhar Verma, Sukruta Prakash Midigeshi, Gaurav Sinha, Arno Solin, Nagarajan Natarajan, and Amit Sharma. 2024 · 2024
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Blendfilter: Advancing retrieval-augmented large language models via query generation blending and knowledge filtering
Haoyu Wang, Ruirui Li, Haoming Jiang, Jinjin Tian, Zhengyang Wang, Chen Luo, Xianfeng Tang, Monica Xiao Cheng, Tuo Zhao, and Jing Gao. 2024 · 2024
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RECOMP: improving retrieval-augmented lms with context compression and selective augmentation
Fangyuan Xu, Weijia Shi, and Eunsol Choi. 2024 · 2024
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Making retrieval-augmented language models robust to irrelevant context
Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant. 2024a · 2024
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Making retrieval-augmented language models robust to irrelevant context
Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant. 2024b · 2024
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Ovm, outcome-supervised value models for planning in mathematical reasoning
Fei Yu, Anningzhe Gao, and Benyou Wang. 2024 · 2024
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