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As demonstrated by GPT-3 and T5, transformers grow in capability as parameter spaces become larger and larger.
Opportunistic data structures with applications
P. Ferragina and G. Manzini · 2000
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Learning to rank for information retrieval
Tie-Yan Liu · 2009
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The probabilistic relevance framework: Bm25 and beyond
Stephen Robertson and Hugo Zaragoza · 2009
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A cascade ranking model for efficient ranked retrieval
Lidan Wang, Jimmy Lin, and Donald Metzler · 2011
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeffrey Dean · 2015
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Ms marco: A human generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng · 2016
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Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 2017
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer · 2017
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Zero-shot relation extraction via reading comprehension
Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer · 2017
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Wizard of wikipedia: Knowledge-powered conversational agents
Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston · 2018
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T-REx: A large scale alignment of natural language with knowledge base triples
Hady Elsahar, Pavlos Vougiouklis, Arslen Remaci, Christophe Gravier, Jonathon Hare, Frederique Laforest, and Elena Simperl · 2018
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Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs
Yu A Malkov and Dmitry A Yashunin · 2018
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Fever: a large-scale dataset for fact extraction and verification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal · 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
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Wizard of wikipedia: Knowledge-powered conversational agents
Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston · 2019
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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, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov · 2019
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Rodrigo Nogueira and Kyunghyun Cho · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer, 2020
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
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CCNet: Extracting high quality monolingual datasets from web crawl data
Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Joulin, and Edouard Grave · 2020
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Scalable zero-shot entity linking with dense entity retrieval
Ledell Wu, Fabio Petroni, Martin Josifoski, Sebastian Riedel, and Luke Zettlemoyer · 2020
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Autoregressive entity retrieval
Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni · 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
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Nboost: Neural boosting search results
Cole Thienes and Jack Pertschuk · 2019
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The fever2. 0 shared task
James Thorne, Andreas Vlachos, Oana Cocarascu, Christos Christodoulopoulos, and Arpit Mittal · 2019
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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
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Realm: Retrieval-augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Ming-Wei Chang · 2020
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SpanBERT: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, and Omer Levy · 2020
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Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer · 2020
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Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Edouard Grave · 2021
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Learning dense representations of phrases at scale
Jinhyuk Lee, Mujeen Sung, Jaewoo Kang, and Danqi Chen · 2021
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Multi-task retrieval for knowledge-intensive tasks
Jean Maillard, Vladimir Karpukhin, Fabio Petroni, Wen-tau Yih, Barlas Oguz, Veselin Stoyanov, and Gargi Ghosh · 2021
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Hindsight: Posterior-guided training of retrievers for improved open-ended generation
Ashwin Paranjape, Omar Khattab, Christopher Potts, Matei Zaharia, and Christopher D Manning · 2021
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KILT: a benchmark for knowledge intensive language tasks
Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, Vassilis Plachouras, Tim Rocktäschel, and Sebastian Riedel · 2021
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The web is your oyster–knowledge-intensive nlp against a very large web corpus
Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Dmytro Okhonko, Samuel Broscheit, Gautier Izacard, Patrick Lewis, Barlas Oğuz, Edouard Grave, Wen-tau Yih, et al · 2021
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Autoregressive search engines: Generating substrings as document identifiers
Michele Bevilacqua, Giuseppe Ottaviano, Patrick Lewis, Wen tau Yih, Sebastian Riedel, and Fabio Petroni · 2022
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