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Large Language Models (LLMs) have catalyzed significant advancements in Natural Language Processing (NLP), yet they encounter challenges such as hallucination and the need for domain-specific knowledge.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
Earlier work this paper cites.
Generating long sequences with sparse transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. 2019 · 1904
Earlier work this paper cites.
Latent retrieval for weakly supervised open domain question answering
Kenton Lee, Ming-Wei Chang, and Kristina Toutanova. 2019 · 1906
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
Earlier work this paper cites.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
Earlier work this paper cites.
Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2019 · 1911
Earlier work this paper cites.
“cloze procedure”: A new tool for measuring readability
Wilson L Taylor. 1953 · 1953
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014 · 1958
Earlier work this paper cites.
Okapi at trec-3
Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, et al. 1995 · 1995
Earlier work this paper cites.
Glu variants improve transformer
Noam Shazeer. 2020 · 2002
Earlier work this paper cites.
Using tf-idf to determine word relevance in document queries
Juan Ramos et al. 2003 · 2003
Earlier work this paper cites.
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 · 2004
Earlier work this paper cites.
Text analysis for ontology and terminology engineering
Nathalie Aussenac-Gilles and Dagobert Sörgel. 2005 · 2005
Earlier work this paper cites.
Retrieval-augmented generation for code summarization via hybrid gnn
Shangqing Liu, Yu Chen, Xiaofei Xie, Jingkai Siow, and Yang Liu. 2020 · 2006
Earlier work this paper cites.
Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Edouard Grave. 2020b · 2007
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. 2008 · 2008
Earlier work this paper cites.
Generation-augmented retrieval for open-domain question answering
Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, and Weizhu Chen. 2020 · 2009
Earlier work this paper cites.
An overview of approaches to extract information from natural language corpora
Frederik Hogenboom, Flavius Frasincar, and Uzay Kaymak. 2010 · 2010
Earlier work this paper cites.
Nearest neighbor machine translation
Urvashi Khandelwal, Angela Fan, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2020 · 2010
Earlier work this paper cites.
Distilling knowledge from reader to retriever for question answering
Gautier Izacard and Edouard Grave. 2020a · 2012
Earlier work this paper cites.
Parnt: a statistic based approach to extract non-taxonomic relationships of ontologies from text
Ivo Serra, Rosario Girardi, and Paulo Novais. 2013 · 2013
Earlier work this paper cites.
Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016 · 2016
Earlier work this paper cites.
Off the beaten path: Let’s replace term-based retrieval with k-nn search
Leonid Boytsov, David Novak, Yury Malkov, and Eric Nyberg. 2016 · 2016
Earlier work this paper cites.
Improving neural language models with a continuous cache
Edouard Grave, Armand Joulin, and Nicolas Usunier. 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
Language modeling with gated convolutional networks
Yann N Dauphin, Angela Fan, Michael Auli, and David Grangier. 2017 · 2017
Earlier work this paper cites.
Searching for activation functions
Prajit Ramachandran, Barret Zoph, and Quoc V Le. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Comparative study of cnn and rnn for natural language processing
Wenpeng Yin, Katharina Kann, Mo Yu, and Hinrich Schütze. 2017 · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Neural argument generation augmented with externally retrieved evidence
Xinyu Hua and Lu Wang. 2018 · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018 · 2018
Earlier work this paper cites.
Fever: a large-scale dataset for fact extraction and verification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
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.
An improved lstm structure for natural language processing
Lirong Yao and Yazhuo Guan. 2018 · 2018
Earlier work this paper cites.
Comparative effectiveness of convolutional neural network (cnn) and recurrent neural network (rnn) architectures for radiology text report classification
Imon Banerjee, Yuan Ling, Matthew C Chen, Sadid A Hasan, Curtis P Langlotz, Nathaniel Moradzadeh, Brian Chapman, Timothy Amrhein, David Mong, Daniel L Rubin, et al. 2019 · 2019
Earlier work this paper cites.
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 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
Memory augmented deep recurrent neural network for video question answering
Chengxiang Yin, Jian Tang, Zhiyuan Xu, and Yanzhi Wang. 2019 · 2019
Earlier work this paper cites.
Root mean square layer normalization
Biao Zhang and Rico Sennrich. 2019 · 2019
Earlier work this paper cites.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020 · 2020
Earlier work this paper cites.
Convbert: Improving bert with span-based dynamic convolution
Zi-Hang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, and Shuicheng Yan. 2020 · 2020
Earlier work this paper cites.
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 · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020 · 2020
Earlier work this paper cites.
Boosting search engines with interactive agents
Leonard Adolphs, Benjamin Boerschinger, Christian Buck, Michelle Chen Huebscher, Massimiliano Ciaramita, Lasse Espeholt, Thomas Hofmann, Yannic Kilcher, Sascha Rothe, Pier Giuseppe Sessa, et al. 2021 · 2021
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Robust retrieval augmented generation for zero-shot slot filling
Michael Glass, Gaetano Rossiello, Md Faisal Mahbub Chowdhury, and Alfio Gliozzo. 2021 · 2021
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Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. 2021 · 2021
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Efficient nearest neighbor language models
Junxian He, Graham Neubig, and Taylor Berg-Kirkpatrick. 2021 · 2021
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Unsupervised dense information retrieval with contrastive learning
Ragas: Automated evaluation of retrieval augmented generation
Shahul Es, Jithin James, Luis Espinosa-Anke, and Steven Schockaert. 2023 · 2023
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Knowledge solver: Teaching llms to search for domain knowledge from knowledge graphs
Chao Feng, Xinyu Zhang, and Zichu Fei. 2023 · 2023
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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 · 2023
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Information retrieval: recent advances and beyond
Kailash A Hambarde and Hugo Proenca. 2023 · 2023
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alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021 · 2021
Cited alongside, same era.
Internet-augmented dialogue generation
Mojtaba Komeili, Kurt Shuster, and Jason Weston. 2021 · 2021
Cited alongside, same era.
Webgpt: Browser-assisted question-answering with human feedback, 2021
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al. 2021 · 2021
Cited alongside, same era.
Large dual encoders are generalizable retrievers
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y Zhao, Yi Luan, Keith B Hall, Ming-Wei Chang, et al. 2021 · 2021
Cited alongside, same era.
Retrieval augmented code generation and summarization
Md Rizwan Parvez, Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021 · 2021
Cited alongside, same era.
Learning to retrieve passages without supervision
Ori Ram, Gal Shachaf, Omer Levy, Jonathan Berant, and Amir Globerson. 2021 · 2021
Cited alongside, same era.
End-to-end training of neural retrievers for open-domain question answering
Devendra Singh Sachan, Mostofa Patwary, Mohammad Shoeybi, Neel Kant, Wei Ping, William L Hamilton, and Bryan Catanzaro. 2021 · 2021
Cited alongside, same era.
Colbertv2: Effective and efficient retrieval via lightweight late interaction
Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, Christopher Potts, and Matei Zaharia. 2021 · 2021
Cited alongside, same era.
Jiaming Han, Renrui Zhang, Wenqi Shao, Peng Gao, Peng Xu, Han Xiao, Kaipeng Zhang, Chris Liu, Song Wen, Ziyu Guo, et al. 2023 · 2023
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Animate-a-story: Storytelling with retrieval-augmented video generation
Yingqing He, Menghan Xia, Haoxin Chen, Xiaodong Cun, Yuan Gong, Jinbo Xing, Yong Zhang, Xintao Wang, Chao Weng, Ying Shan, et al. 2023 · 2023
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Fid-light: Efficient and effective retrieval-augmented text generation
Sebastian Hofstätter, Jiecao Chen, Karthik Raman, and Hamed Zamani. 2023 · 2023
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Reveal: Retrieval-augmented visual-language pre-training with multi-source multimodal knowledge memory
Ziniu Hu, Ahmet Iscen, Chen Sun, Zirui Wang, Kai-Wei Chang, Yizhou Sun, Cordelia Schmid, David A Ross, and Alireza Fathi. 2023 · 2023
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Make-an-audio: Text-to-audio generation with prompt-enhanced diffusion models
Rongjie Huang, Jiawei Huang, Dongchao Yang, Yi Ren, Luping Liu, Mingze Li, Zhenhui Ye, Jinglin Liu, Xiang Yin, and Zhou Zhao. 2023 · 2023
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Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
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Knowledge graph-augmented language models for knowledge-grounded dialogue generation
Minki Kang, Jin Myung Kwak, Jinheon Baek, and Sung Ju Hwang. 2023 · 2023
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Zachary Levonian, Chenglu Li, Wangda Zhu, Anoushka Gade, Owen Henkel, Millie-Ellen Postle, and Wanli Xing. 2023 · 2023
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Recall: A benchmark for llms robustness against external counterfactual knowledge
Yi Liu, Lianzhe Huang, Shicheng Li, Sishuo Chen, Hao Zhou, Fandong Meng, Jie Zhou, and Xu Sun. 2023 · 2023
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Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, et al. 2023 · 2023
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In-context retrieval-augmented language models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023 · 2023
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Ares: An automated evaluation framework for retrieval-augmented generation systems
Jon Saad-Falcon, Omar Khattab, Christopher Potts, and Matei Zaharia. 2023 · 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 · 2023
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Improving the domain adaptation of retrieval augmented generation (rag) models for open domain question answering
Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, Tharindu Kaluarachchi, Rajib Rana, and Suranga Nanayakkara. 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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Haoyan Yang, Zhitao Li, Yong Zhang, Jianzong Wang, Ning Cheng, Ming Li, and Jing Xiao. 2023 · 2023
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Making retrieval-augmented language models robust to irrelevant context
Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant. 2023 · 2023
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Retrieval-enhanced generative model for large-scale knowledge graph completion
Donghan Yu and Yiming Yang. 2023 · 2023
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Improving language models via plug-and-play retrieval feedback
Wenhao Yu, Zhihan Zhang, Zhenwen Liang, Meng Jiang, and Ashish Sabharwal. 2023 · 2023
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Attention satisfies: A constraint-satisfaction lens on factual errors of language models
Mert Yuksekgonul, Varun Chandrasekaran, Erik Jones, Suriya Gunasekar, Ranjita Naik, Hamid Palangi, Ece Kamar, and Besmira Nushi. 2023 · 2023
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Retrieving multimodal information for augmented generation: A survey
Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, et al. 2023 · 2023
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Benchmarking large language models in retrieval-augmented generation
Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. 2024 · 2024
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Alpacafarm: A simulation framework for methods that learn from human feedback
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Retrieval-generation synergy augmented large language models
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Mathematical discoveries from program search with large language models
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