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Performing automatic reformulations of a user's query is a popular paradigm used in information retrieval (IR) for improving effectiveness -- as exemplified by the pseudo-relevance feedback approaches, which expand the query in order to alleviate the vocabulary mismatch problem.
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 · 1901
Earlier work this paper cites.
Context-aware document term weighting for ad-hoc search. In Proceedings of WWW . 1897–1907
Zhuyun Dai and Jamie Callan. 2020 · 1907
Earlier work this paper cites.
Query expansion using word embeddings. In Proceedings of CIKM . 1929–1932
Saar Kuzi, Anna Shtok, and Oren Kurland. 2016 · 1932
Earlier work this paper cites.
Few-Shot Generative Conversational Query Rewriting. In Proceedings of SIGIR . 1933–1936
Shi Yu, Jiahua Liu, Jingqin Yang, Chenyan Xiong, Paul Bennett, Jianfeng Gao, and Zhiyuan Liu. 2020 · 1936
Earlier work this paper cites.
An association thesaurus for information retrieval. In Intelligent multimedia information retrieval systems and management . 146–160
Yufent Jing and W.B. Croft. 1994 · 1994
Earlier work this paper cites.
Probabilistic models of information retrieval based on measuring the divergence from randomness
Gianni Amati and Cornelis Joost van Rijsbergen. 2002 · 2002
Earlier work this paper cites.
Passage retrieval based on language models. In Proceedings of CIKM . 375–382
Xiaoyong Liu and W Bruce Croft. 2002 · 2002
Earlier work this paper cites.
Probability models for information retrieval based on divergence from randomness
Giambattista Amati. 2003 · 2003
Earlier work this paper cites.
UMass at TREC 2004: Novelty and HARD. In Proceedings of TREC
Nasreen Abdul-Jaleel, James Allan, W Bruce Croft, Fernando Diaz, Leah Larkey, Xiaoyan Li, Mark D Smucker, and Courtney Wade. 2004 · 2004
Earlier work this paper cites.
Overview of the TREC 2004 Terabyte Track
Charles LA Clarke, Nick Craswell, and Ian Soboroff. 2004 · 2004
Earlier work this paper cites.
A framework for selective query expansion. In Proceedings of CIKM . 236–237
Steve Cronen-Townsend, Yun Zhou, and W Bruce Croft. 2004 · 2004
Earlier work this paper cites.
Overview of the TREC 2004 Robust Track,. In Proceedings of TREC
Ellen M Voorhees. 2004 · 2004
Earlier work this paper cites.
Document expansion versus query expansion for ad-hoc retrieval. In Proceedings of ADCS . 34–41
Bodo Billerbeck and Justin Zobel. 2005 · 2005
Earlier work this paper cites.
Generating query substitutions. In Proceedings of WWW . 387–396
Rosie Jones, Benjamin Rey, Omid Madani, and Wiley Greiner. 2006 · 2006
Earlier work this paper cites.
Random walks on the click graph. In Proceedings of SIGIR . 239–246
Nick Craswell and Martin Szummer. 2007 · 2007
Earlier work this paper cites.
Statistical Machine Translation for Query Expansion in Answer Retrieval. In Proceedings of ACL . 464–471
Stefan Riezler, Alexander Vasserman, Ioannis Tsochantaridis, Vibhu Mittal, and Yi Liu. 2007 · 2007
Earlier work this paper cites.
Search engines: Information retrieval in practice . Vol. 520
W Bruce Croft, Donald Metzler, and Trevor Strohman. 2010 · 2010
Earlier work this paper cites.
Query Expansion with Locally-Trained Word Embeddings. In Proceedings of ACL . 367–377
Fernando Diaz, Bhaskar Mitra, and Nick Craswell. 2016 · 2016
Earlier work this paper cites.
Learning to attend, copy, and generate for session-based query suggestion. In Proceedings of CIKM . 1747–1756
Mostafa Dehghani, Sascha Rothe, Enrique Alfonseca, and Pascal Fleury. 2017 · 2017
Earlier work this paper cites.
OpenNMT: Open-Source Toolkit for Neural Machine Translation. In Proceedings of ACL . 67–72
Guillaume Klein, Yoon Kim, Yuntian Deng, Jean Senellart, and Alexander M Rush. 2017 · 2017
Earlier work this paper cites.
Task-Oriented Query Reformulation with Reinforcement Learning. In Proceedings of EMNLP . 574–583
Rodrigo Nogueira and Kyunghyun Cho. 2017 · 2017
Earlier work this paper cites.
Attention is all you need. In Proceedings of NeurIPS . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval. In Proceedings of EMNLP . 4482–4491
Canjia Li, Yingfei Sun, Ben He, Le Wang, Kai Hui, Andrew Yates, Le Sun, and Jungang Xu. 2018 · 2018
Cited alongside, same era.
Overview of the TREC 2019 deep learning track. In Proceedings of TREC
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M Voorhees. 2020 · 2019
Cited alongside, same era.
Deeper text understanding for IR with contextual neural language modeling. In Proceedings of SIGIR . 985–988
Zhuyun Dai and Jamie Callan. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of ACL . 4171–4186
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Unsupervised FAQ Retrieval with Question Generation and BERT. In Proceedings of ACL . 807–812
Yosi Mass, Boaz Carmeli, Haggai Roitman, and David Konopnicki. 2020 · 2020
Later among the works it cites.
A Reinforcement Learning Framework for Relevance Feedback. In Proceedings of SIGIR . 59–68
Ali Montazeralghaem, Hamed Zamani, and James Allan. 2020 · 2020
Later among the works it cites.
Document ranking with a pretrained sequence-to-sequence model. In Proceedings of EMNLP: Findings
Rodrigo Nogueira, Zhiying Jiang, and Jimmy Lin. 2020 · 2020
Later among the works it 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. 2020 · 2020
Later among the works it cites.
Deep Reinforced Query Reformulation for Information Retrieval
Xiao Wang, Craig Macdonald, and Iadh Ounis. 2020 · 2020
Later among the works it cites.
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The neural hype and comparisons against weak baselines
Jimmy Lin. 2019 · 2019
Cited alongside, same era.
CEDR: Contextualized embeddings for document ranking. In Proceedings of SIGIR . 1101–1104
Sean MacAvaney, Andrew Yates, Arman Cohan, and Nazli Goharian. 2019 · 2019
Cited alongside, same era.
From doc2query to docTTTTTquery
Rodrigo Nogueira and Jimmy Lin. 2019 · 2019
Cited alongside, same era.
From doc2query to docTTTTTquery
Rodrigo Nogueira, Jimmy Lin, and AI Epistemic. 2019a · 2019
Cited alongside, same era.
Multi-stage document ranking with BERT
Rodrigo Nogueira, Wei Yang, Kyunghyun Cho, and Jimmy Lin. 2019b · 2019
Cited alongside, same era.
Document expansion by query prediction
Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. 2019c · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
BERT-QE: Contextualized Query Expansion for Document Re-ranking. In Proceedings of EMNLP: Findings . 4718–4728
Zhi Zheng, Kai Hui, Ben He, Xianpei Han, Le Sun, and Andrew Yates. 2020 · 2020
Later among the works it cites.
Generation-augmented retrieval for open-domain question answering. In Proceedings of ACL . 4089–4100
Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, and Weizhu Chen. 2021 · 2021
Later among the works it cites.
CEQE: Contextualized Embeddings for Query Expansion. In Proceedings of ECIR
Shahrzad Naseri, Jeffrey Dalton, Andrew Yates, and James Allan. 2021 · 2021
Later among the works it cites.
The expando-mono-duo design pattern for text ranking with pretrained sequence-to-sequence models
Ronak Pradeep, Rodrigo Nogueira, and Jimmy Lin. 2021 · 2021
Later among the works it cites.
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature
Yu Wang, Jinchao Li, Tristan Naumann, Chenyan Xiong, Hao Cheng, Robert Tinn, Cliff Wong, Naoto Usuyama, Richard Rogahn, Zhihong Shen, Yang Qin, Eric Horvitz, Paul Bennett, Jianfeng Gao, and Hoifung Poon. 2021a · 2021
Later among the works it cites.
Finetuned language models are zero-shot learners. In Proceedings of ICLR
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
Later among the works it cites.
Approximate nearest neighbor negative contrastive learning for dense text retrieval. In Proceedings of ICLR
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, and Arnold Overwijk. 2021 · 2021
Later among the works it cites.
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback. In Proceedings of CIKM . 599–612
HongChien Yu, Chenyan Xiong, and Jamie Callan. 2021 · 2021
Later among the works it cites.
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
Later among the works it cites.
Pseudo relevance feedback with deep language models and dense retrievers: Successes and pitfalls
Hang Li, Ahmed Mourad, Shengyao Zhuang, Bevan Koopman, and Guido Zuccon. 2022 · 2022
Later among the works it cites.
Document expansion baselines and learned sparse lexical representations for ms marco v1 and v2. In Proceedings of SIGIR . 3187–3197
Xueguang Ma, Ronak Pradeep, Rodrigo Nogueira, and Jimmy Lin. 2022 · 2022
Later among the works it cites.
ColBERT-PRF: Semantic Pseudo-Relevance Feedback for Dense Passage and Document Retrieval
Xiao Wang, Craig Macdonald, Nicola Tonellotto, and Iadh Ounis. 2022 · 2022
Later among the works it cites.
Emergent Abilities of Large Language Models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022
Later among the works it cites.
Doc2Query–: When Less is More. In Advances in Information Retrieval - 45th European Conference on Information Retrieval, ECIR 2023, Dublin, Ireland, April 2-6, 2023, Proceedings, Part II (Lecture Notes in Computer Science, Vol. 13981) . Springer, 414–422
Mitko Gospodinov, Sean MacAvaney, and Craig Macdonald. 2023 · 2023
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Generative Relevance Feedback with Large Language Models
Iain Mackie, Shubham Chatterjee, and Jeffrey Dalton. 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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