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Query expansion, pivotal in search engines, enhances the representation of user information needs with additional terms.
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
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Relevance feedback in information retrieval
Joseph John Rocchio Jr. 1971 · 1971
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On term selection for query expansion
Stephen E Robertson. 1990 · 1990
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Concept based query expansion
Yonggang Qiu and Hans-Peter Frei. 1993 · 1993
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Query expansion using lexical-semantic relations
Ellen M Voorhees. 1994 · 1994
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Query expansion
Efthimis N Efthimiadis. 1996 · 1996
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Probabilistic models of information retrieval based on measuring the divergence from randomness
Gianni Amati and Cornelis Joost Van Rijsbergen. 2002 · 2002
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Multimedia search with pseudo-relevance feedback
Rong Yan, Alexander Hauptmann, and Rong Jin. 2003 · 2003
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Umass at trec 2004: Novelty and hard
Nasreen Abdul-Jaleel, James Allan, W Bruce Croft, Fernando Diaz, Leah Larkey, Xiaoyan Li, Mark D Smucker, and Courtney Wade. 2004 · 2004
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Semantic term matching in axiomatic approaches to information retrieval
Hui Fang and ChengXiang Zhai. 2006 · 2006
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A review of ontology based query expansion
Jagdev Bhogal, Andrew MacFarlane, and Peter Smith. 2007 · 2007
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Selecting good expansion terms for pseudo-relevance feedback
Guihong Cao, Jian-Yun Nie, Jianfeng Gao, and Stephen Robertson. 2008 · 2008
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Positional relevance model for pseudo-relevance feedback
Yuanhua Lv and ChengXiang Zhai. 2010 · 2010
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A survey of automatic query expansion in information retrieval
Claudio Carpineto and Giovanni Romano. 2012 · 2012
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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 · 2016
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Query expansion techniques for information retrieval: a survey
Hiteshwar Kumar Azad and Akshay Deepak. 2019 · 2019
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Reproducing and generalizing semantic term matching in axiomatic information retrieval
Peilin Yang and Jimmy Lin. 2019 · 2019
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Overview of the trec 2020 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, and Daniel Campos. 2021 · 2020
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Declarative experimentation ininformation retrieval using pyterrier
Craig Macdonald and Nicola Tonellotto. 2020 · 2020
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Neural text generation for query expansion in information retrieval
Vincent Claveau. 2021 · 2021
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Sentence-t5: Scalable sentence encoders from pre-trained text-to-text models
Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Sgpt: Gpt sentence embeddings for semantic search
Niklas Muennighoff. 2022 · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
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Graph of thoughts: Solving elaborate problems with large language models
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Michal Podstawski, Hubert Niewiadomski, Piotr Nyczyk, et al. 2023 · 2023
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Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, Ji Ma, Keith B Hall, Daniel Cer, and Yinfei Yang. 2021 · 2021
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BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models
Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. 2021 · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
Cited alongside, same era.
Inpars: Data augmentation for information retrieval using large language models
Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, and Rodrigo Nogueira. 2022 · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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Promptagator: Few-shot dense retrieval from 8 examples
Zhuyun Dai, Vincent Y Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B Hall, and Ming-Wei Chang. 2022 · 2022
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A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui. 2022 · 2022
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Qian Dong, Yiding Liu, Qingyao Ai, Zhijing Wu, Haitao Li, Yiqun Liu, Shuaiqiang Wang, Dawei Yin, and Shaoping Ma. 2023 · 2023
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Query expansion by prompting large language models
Rolf Jagerman, Honglei Zhuang, Zhen Qin, Xuanhui Wang, and Michael Bendersky. 2023 · 2023
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Agent4ranking: Semantic robust ranking via personalized query rewriting using multi-agent llm
Xiaopeng Li, Lixin Su, Pengyue Jia, Xiangyu Zhao, Suqi Cheng, Junfeng Wang, and Dawei Yin. 2023 · 2023
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The flan collection: Designing data and methods for effective instruction tuning
Shayne Longpre, Le Hou, Tu Vu, Albert Webson, Hyung Won Chung, Yi Tay, Denny Zhou, Quoc V Le, Barret Zoph, Jason Wei, et al. 2023 · 2023
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Kelong Mao, Zhicheng Dou, Haonan Chen, Fengran Mo, and Hongjin Qian. 2023 · 2023
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Is chatgpt good at search? investigating large language models as re-ranking agent
Weiwei Sun, Lingyong Yan, Xinyu Ma, Pengjie Ren, Dawei Yin, and Zhaochun Ren. 2023 · 2023
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Query2doc: Query expansion with large language models
Liang Wang, Nan Yang, and Furu Wei. 2023 · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan. 2023 · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al. 2023 · 2023
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Large language models for information retrieval: A survey
Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Zhicheng Dou, and Ji-Rong Wen. 2023 · 2023
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Rankt5: Fine-tuning t5 for text ranking with ranking losses
Honglei Zhuang, Zhen Qin, Rolf Jagerman, Kai Hui, Ji Ma, Jing Lu, Jianmo Ni, Xuanhui Wang, and Michael Bendersky. 2023 · 2023
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