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Recent studies demonstrate that query expansions generated by large language models (LLMs) can considerably enhance information retrieval systems by generating hypothetical documents that answer the queries as expansions.
On term selection for query expansion
Stephen Robertson. 1990 · 1990
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Concept based query expansion
Yonggang Qiu and Hans-Peter Frei. 1993 · 1993
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Relevance-Based language models
Victor Lavrenko and W. Bruce Croft. 2001 · 2001
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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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Overview of the trec 2019 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M. Voorhees. 2020 · 2003
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Umass at trec 2004: Novelty and hard
Nasreen Abdul Jaleel, James Allan, W. Bruce Croft, Fernando Diaz, Leah S. Larkey, Xiaoyan Li, Mark D. Smucker, and Courtney Wade. 2004 · 2004
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A review of ontology based query expansion
J. Bhogal, A. Macfarlane, and P. Smith. 2007 · 2007
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Ms marco: A human generated machine reading comprehension dataset
Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, et al. 2016 · 2016
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Overview of the trec 2020 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M. Voorhees. 2021 · 2020
Earlier work this paper cites.
Pyserini: A python toolkit for reproducible information retrieval research with sparse and dense representations
Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Precise zero-shot dense retrieval without relevance labels
Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. 2022 · 2022
Cited alongside, same era.
Unsupervised dense information retrieval with contrastive learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2022 · 2022
Cited alongside, same era.
RealTime QA: What’s the answer right now?
Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, Ronan Le Bras, Akari Asai, Xinyan Yu, Dragomir Radev, Noah A. Smith, Yejin Choi, and Kentaro Inui. 2022 · 2022
Large language models struggle to learn long-tail knowledge
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2023 · 2023
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Is ChatGPT good at search? investigating large language models as re-ranking agents
Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, and Zhaochun Ren. 2023 · 2023
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Large language models can accurately predict searcher preferences
Paul Thomas, Seth Spielman, Nick Craswell, and Bhaskar Mitra. 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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Orion Weller, Kyle Lo, David Wadden, Dawn J Lawrie, Benjamin Van Durme, Arman Cohan, and Luca Soldaini. 2023 · 2023
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Cited alongside, same era.
Data contamination: From memorization to exploitation
Inbal Magar and Roy Schwartz. 2022 · 2022
Cited alongside, same era.
Perspectives on large language models for relevance judgment
Guglielmo Faggioli, Laura Dietz, Charles L. A. Clarke, Gianluca Demartini, Matthias Hagen, Claudia Hauff, Noriko Kando, Evangelos Kanoulas, Martin Potthast, Benno Stein, and Henning Wachsmuth. 2023 · 2023
Cited alongside, same era.
Query expansion by prompting large language models
Rolf Jagerman, Honglei Zhuang, Zhen Qin, Xuanhui Wang, and Michael Bendersky. 2023 · 2023
Cited alongside, same era.
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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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Siren’s song in the ai ocean: A survey on hallucination in large language models
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, and Shuming Shi. 2023 · 2023
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Generative relevance feedback with large language models
Iain Mackie, Shubham Chatterjee, and Jeffrey Dalton. 2023 · 2031
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