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Zero-shot text rankers powered by recent LLMs achieve remarkable ranking performance by simply prompting.
Multi-stage document ranking with BERT
Rodrigo Nogueira, Wei Yang, Kyunghyun Cho, and Jimmy Lin. 2019 · 1910
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Selecting the number of response categories for a likert-type scale
Nicholas J Birkett. 1986 · 1986
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Cumulated gain-based evaluation of IR techniques
Kalervo Järvelin and Jaana Kekäläinen. 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. 2020b · 2003
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Learning-to-rank with BERT in TF-Ranking
Shuguang Han, Xuanhui Wang, Mike Bendersky, and Marc Najork. 2020 · 2004
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The trec robust retrieval track
Ellen M Voorhees. 2005 · 2005
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Learning to Rank for Information Retrieval
Tie-Yan Liu. 2009 · 2009
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Predicting query performance by query-drift estimation
Anna Shtok, Oren Kurland, David Carmel, Fiana Raiber, and Gad Markovits. 2012 · 2012
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Contemporary guidance for stated preference studies
Robert J Johnston, Kevin J Boyle, Wiktor Adamowicz, Jeff Bennett, Roy Brouwer, Trudy Ann Cameron, W Michael Hanemann, Nick Hanley, Mandy Ryan, Riccardo Scarpa, et al. 2017 · 2017
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On fine-grained relevance scales
Kevin Roitero, Eddy Maddalena, Gianluca Demartini, and Stefano Mizzaro. 2018 · 2018
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Choppy: Cut transformer for ranked list truncation
Dara Bahri, Yi Tay, Che Zheng, Donald Metzler, and Andrew Tomkins. 2020 · 2020
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Overview of the TREC 2020 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, and Daniel Campos. 2020a · 2020
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Document ranking with a pretrained sequence-to-sequence model
Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020 · 2020
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Not all relevance scores are equal: Efficient uncertainty and calibration modeling for deep retrieval models
Daniel Cohen, Bhaskar Mitra, Oleg Lesota, Navid Rekabsaz, and Carsten Eickhoff. 2021 · 2021
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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
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Are neural rankers still outperformed by gradient boosted decision trees?
Zhen Qin, Le Yan, Honglei Zhuang, Yi Tay, Rama Kumar Pasumarthi, Xuanhui Wang, Michael Bendersky, and Marc Najork. 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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Representing numbers in NLP: a survey and a vision
Avijit Thawani, Jay Pujara, Filip Ilievski, and Pedro Szekely. 2021 · 2021
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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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Holistic evaluation of language models
Percy Liang, Rishi Bommasani, Tony Lee, Dimitris Tsipras, Dilara Soylu, Michihiro Yasunaga, Yian Zhang, Deepak Narayanan, Yuhuai Wu, Ananya Kumar, et al. 2023 · 2023
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G-eval: Nlg evaluation using gpt-4 with better human alignment
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023 · 2023
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Zero-shot listwise document reranking with a large language model
Xueguang Ma, Xinyu Zhang, Ronak Pradeep, and Jimmy Lin. 2023 · 2023
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OpenAI. 2023 · 2023
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Trec-covid: constructing a pandemic information retrieval test collection
Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, and Lucy Lu Wang. 2021 · 2021
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Ensemble distillation for BERT-based ranking models
Honglei Zhuang, Zhen Qin, Shuguang Han, Xuanhui Wang, Michael Bendersky, and Marc Najork. 2021 · 2021
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Continuous and binary sets of responses differ in the field
Noelia Rivera-Garrido, MP Ramos-Sosa, Michela Accerenzi, and Pablo Brañas-Garza. 2022 · 2022
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Improving passage retrieval with zero-shot question generation
Devendra Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, and Luke Zettlemoyer. 2022 · 2022
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UL2: Unifying language learning paradigms
Yi Tay, Mostafa Dehghani, Vinh Q Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal Schuster, Steven Zheng, et al. 2022 · 2022
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Perspectives on large language models for relevance judgment
Guglielmo Faggioli, Laura Dietz, Charles LA Clarke, Gianluca Demartini, Matthias Hagen, Claudia Hauff, Noriko Kando, Evangelos Kanoulas, Martin Potthast, Benno Stein, et al. 2023 · 2023
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Recommender systems in the era of large language models (LLMs)
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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
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A survey on large language models for recommendation
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Learning list-level domain-invariant representations for ranking
Ruicheng Xian, Honglei Zhuang, Zhen Qin, Hamed Zamani, Jing Lu, Ji Ma, Kai Hui, Han Zhao, Xuanhui Wang, and Michael Bendersky. 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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Can query expansion improve generalization of strong cross-encoder rankers?
Minghan Li, Honglei Zhuang, Kai Hui, Zhen Qin, Jimmy Lin, Rolf Jagerman, Xuanhui Wang, and Michael Bendersky. 2024 · 2024
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Large language models are effective text rankers with pairwise ranking prompting
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A setwise approach for effective and highly efficient zero-shot ranking with large language models
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