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Large Language Models (LLMs) have shown impressive results on a variety of text understanding tasks.
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.
Document expansion by query prediction
Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. 2019 · 1904
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. 2019 · 1910
Earlier work this paper cites.
A taxonomy of web search
Andrei Broder. 2002 · 2002
Earlier work this paper cites.
Query type classification for web document retrieval
In-Ho Kang and GilChang Kim. 2003 · 2003
Earlier work this paper cites.
Learning-to-rank with bert in tf-ranking
Shuguang Han, Xuanhui Wang, Mike Bendersky, and Marc Najork. 2020 · 2004
Earlier work this paper cites.
The intention behind web queries
Ricardo Baeza-Yates, Liliana Calderón-Benavides, and Cristina González-Caro. 2006 · 2006
Earlier work this paper cites.
Improving the estimation of relevance models using large external corpora
Fernando Diaz and Donald Metzler. 2006 · 2006
Earlier work this paper cites.
Robust classification of rare queries using web knowledge
Andrei Z Broder, Marcus Fontoura, Evgeniy Gabrilovich, Amruta Joshi, Vanja Josifovski, and Tong Zhang. 2007 · 2007
Earlier work this paper cites.
Leveraging passage retrieval with generative models for open domain question answering
Gautier Izacard and Edouard Grave. 2020 · 2007
Earlier work this paper cites.
Search advertising using web relevance feedback
Andrei Z. Broder, Peter Ciccolo, Marcus Fontoura, Evgeniy Gabrilovich, Vanja Josifovski, and Lance Riedel. 2008 · 2008
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Determining the informational, navigational, and transactional intent of web queries
Bernard J Jansen, Danielle L Booth, and Amanda Spink. 2008 · 2008
Cited alongside, same era.
Classifying the user intent of web queries using k-means clustering
Ashish Kathuria, Bernard J Jansen, Carolyn Hafernik, and Amanda Spink. 2010 · 2010
Cited alongside, same era.
Deriving query intents from web search engine queries
Dirk Lewandowski, Jessica Drechsler, and Sonja Von Mach. 2012 · 2012
Cited alongside, same era.
Exploring effective features for recognizing the user intent behind web queries
Alejandro Figueroa. 2015 · 2015
Cited alongside, same era.
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
Later among the works it cites.
Improved knowledge distillation via teacher assistant
Seyed Iman Mirzadeh, Mehrdad Farajtabar, Ang Li, Nir Levine, Akihiro Matsukawa, and Hassan Ghasemzadeh. 2020 · 2020
Later among the works it cites.
Query Segmentation and Tagging , pages 43–67. Springer International Publishing, Cham
Xuanhui Wang. 2020 · 2020
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Query understanding via intent description generation
Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, and Xueqi Cheng. 2020 · 2020
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Knowledge distillation: A survey
Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. 2021 · 2021
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Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al. 2015 · 2015
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A customised grammar framework for query classification
Alaa Mohasseb, Mohamed Bader-El-Den, and Mihaela Cocea. 2019 · 2019
Cited alongside, same era.
From doc2query to doctttttquery
Rodrigo Nogueira and Jimmy Lin. 2019 · 2019
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Orcas: 20 million clicked query-document pairs for analyzing search
Nick Craswell, Daniel Campos, Bhaskar Mitra, Emine Yilmaz, and Bodo Billerbeck. 2020 · 2020
Cited alongside, same era.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang. 2020 · 2020
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Fid-light: Efficient and effective retrieval-augmented text generation
Sebastian Hofstätter, Jiecao Chen, Karthik Raman, and Hamed Zamani. 2022a
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Multi-task retrieval-augmented text generation with relevance sampling
Sebastian Hofstätter, Jiecao Chen, Karthik Raman, and Hamed Zamani. 2022b
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mT5: A massively multilingual pre-trained text-to-text transformer
Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021 · 2021
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ORCAS-i
Daria Alexander, Wojciech Kusa, and Arjen P. de Vries. 2022 · 2022
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Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et al. 2022 · 2022
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
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Few-shot learning with retrieval augmented language models
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave. 2022 · 2022
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