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Retrieval augmented generation (RAG) has been applied in many scenarios to augment large language models (LLMs) with external documents provided by retrievers.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, and et al. 2019 · 1912
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Kuttler, Mike Lewis, and et al. 2020 · 2005
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Yuma Koizumi, Yasunori Ohishi, Daisuke Niizumi, Daiki Takeuchi, and Masahiro Yasuda. 2020 · 2012
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
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Attention is all you need
Ashish Vaswani, Noam M. Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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DuReader: a Chinese machine reading comprehension dataset from real-world applications
Wei He, Kai Liu, Jing Liu, Yajuan Lyu, Shiqi Zhao, Xinyan Xiao, Yuan Liu, Yizhong Wang, and et al. 2018 · 2018
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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 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Natural questions: A benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, and et al. 2019 · 2019
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 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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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, and et al. 2020 · 2020
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Lora: Low-rank adaptation of large language models
J. Edward Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, and Weizhu Chen. 2021 · 2021
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End-to-end training of multi-document reader and retriever for open-domain question answering
Devendra Singh Sachan, Siva Reddy, William L. Hamilton, Chris Dyer, and Dani Yogatama. 2021 · 2021
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Musique: Multihop questions via single-hop question composition
H. Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2021 · 2021
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Parishad BehnamGhader, Santiago Miret, and Siva Reddy. 2022 · 2022
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Re2G: Retrieve, rerank, generate
Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, Ankita Naik, Pengshan Cai, and Alfio Gliozzo. 2022 · 2022
Cited alongside, same era.
Atlas: Few-shot learning with retrieval augmented language models
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane A. Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2022 · 2022
Cited alongside, same era.
Large language models struggle to learn long-tail knowledge
Nikhil Kandpal, H. Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2022 · 2022
Cited alongside, same era.
Lavis: A library for language-vision intelligence
Dongxu Li, Junnan Li, Hung Le, Guangsen Wang, Silvio Savarese, and Steven C. H. Hoi. 2022 · 2022
Cited alongside, same era.
Incorporating retrieval information into the truncation of ranking lists for better legal search
Yixiao Ma, Qingyao Ai, Yueyue Wu, Yunqiu Shao, Yiqun Liu, M. Zhang, and Shaoping Ma. 2022 · 2022
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin R. Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, and et al. 2023 · 2023
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C-pack: Packaged resources to advance general chinese embedding
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff. 2023 · 2023
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Recomp: Improving retrieval-augmented lms with compression and selective augmentation
Fangyuan Xu, Weijia Shi, and Eunsol Choi. 2023 · 2023
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Retrieval-augmented multimodal language modeling
Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Rich James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, and Wen tau Yih. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
Text and code embeddings by contrastive pre-training
Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas A. Tezak, Jong Wook Kim, and et al. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, and et al. 2022 · 2022
Cited alongside, same era.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Huai hsin Chi, F. Xia, Quoc Le, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Dense text retrieval based on pretrained language models: A survey
Wayne Xin Zhao, Jing Liu, Ruiyang Ren, and Ji rong Wen. 2022 · 2022
Cited alongside, same era.
Gpt-4 technical report
OpenAI Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, and et al. 2023 · 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 · 2023
Cited alongside, same era.
RegaVAE: A retrieval-augmented Gaussian mixture variational auto-encoder for language modeling
Jingcheng Deng, Liang Pang, Huawei Shen, and Xueqi Cheng. 2023 · 2023
Cited alongside, same era.
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, and et al. 2023 · 2023
Later among the works it cites.
Seven failure points when engineering a retrieval augmented generation system
Scott Barnett, Stefanus Kurniawan, Srikanth Thudumu, Zach Brannelly, and Mohamed Abdelrazek. 2024 · 2024
Closest in time.
Zheng Cai, Maosong Cao, Haojiong Chen, Kai Chen, Keyu Chen, Xin Chen, Xun Chen, Zehui Chen, Zhi Chen, Pei Chu, Xiao wen Dong, and et al. 2024 · 2024
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G-retriever: Retrieval-augmented generation for textual graph understanding and question answering
Xiaoxin He, Yijun Tian, Yifei Sun, N. Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi. 2024 · 2024
Closest in time.
Atlas: few-shot learning with retrieval augmented language models
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, and et al. 2024 · 2024
Closest in time.
Bridging the preference gap between retrievers and llms
Zixuan Ke, Weize Kong, Cheng Li, Mingyang Zhang, Qiaozhu Mei, and Michael Bendersky. 2024 · 2024
Closest in time.
How easily do irrelevant inputs skew the responses of large language models?
Siye Wu, Jian Xie, Jiangjie Chen, Tinghui Zhu, Kai Zhang, and Yanghua Xiao. 2024 · 2024
Closest in time.
List-aware reranking-truncation joint model for search and retrieval-augmented generation
Shicheng Xu, Liang Pang, Jun Xu, Huawei Shen, and Xueqi Cheng. 2024 · 2024
Closest in time.
Corrective retrieval augmented generation
Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, and Zhen-Hua Ling. 2024 · 2024
Closest in time.
Milecut: A multi-view truncation framework for legal case retrieval
Fuda Ye and Shuangyin Li. 2024 · 2024
Closest in time.
Raft: Adapting language model to domain specific rag
Tianjun Zhang, Shishir G. Patil, Naman Jain, Sheng Shen, Matei A. Zaharia, Ion Stoica, and Joseph E. Gonzalez. 2024 · 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 · 2024
Closest in time.