Fetching the paper…
Reading the bibliography…
This paper introduces a simple yet effective query expansion approach, denoted as query2doc, to improve both sparse and dense retrieval systems.
Document expansion by query prediction
Rodrigo Nogueira, Wei Yang, Jimmy J. Lin, and Kyunghyun Cho. 2019 · 1904
Earlier work this paper cites.
Relevance feedback in information retrieval
J. J. Rocchio. 1971 · 1971
Earlier work this paper cites.
WordNet: A lexical database for English
George A. Miller. 1992 · 1992
Earlier work this paper cites.
Relevance-based language models
Victor Lavrenko and W. Bruce Croft. 2001 · 2001
Earlier work this paper cites.
Overview of the trec 2019 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M Voorhees. 2020a · 2003
Earlier work this paper cites.
Overview of the trec 2020 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Fernando Campos, and Ellen M. Voorhees. 2020b · 2003
Earlier work this paper cites.
A comparative study of methods for estimating query language models with pseudo feedback
Yuanhua Lv and ChengXiang Zhai. 2009 · 2009
Earlier work this paper cites.
A full-text learning to rank dataset for medical information retrieval
Vera Boteva, Demian Gholipour, Artem Sokolov, and Stefan Riezler. 2016 · 2016
Earlier work this paper cites.
Ms marco: A human generated machine reading comprehension dataset
Daniel Fernando Campos, Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, Li Deng, and Bhaskar Mitra. 2016 · 2016
Earlier work this paper cites.
Dbpedia-entity v2: A test collection for entity search
Faegheh Hasibi, Fedor Nikolaev, Chenyan Xiong, Krisztian Balog, Svein Erik Bratsberg, Alexander Kotov, and Jamie Callan. 2017 · 2017
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Earlier work this paper cites.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
Earlier work this paper cites.
Fact or fiction: Verifying scientific claims
David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, and Hannaneh Hajishirzi. 2020 · 2020
Cited alongside, same era.
Splade: Sparse lexical and expansion model for first stage ranking
Thibault Formal, Benjamin Piwowarski, and Stéphane Clinchant. 2021 · 2021
Cited alongside, same era.
Condenser: a pre-training architecture for dense retrieval
Luyu Gao and Jamie Callan. 2021 · 2021
Cited alongside, same era.
Jimmy J. Lin and Xueguang Ma. 2021 · 2021
Cited alongside, same era.
Pyserini: A python toolkit for reproducible information retrieval research with sparse and dense representations
Jimmy J. Lin, Xueguang Ma, Sheng-Chieh Lin, Jheng-Hong Yang, Ronak Pradeep, Rodrigo Nogueira, and David R. Cheriton. 2021 · 2021
Cited alongside, same era.
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, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam M. Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Benton C. Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier García, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Díaz, Orhan Firat, Michele Catasta, Jason Wei, Kathleen S. Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2022 · 2022
Later among the works it cites.
Precise zero-shot dense retrieval without relevance labels
Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. 2022 · 2022
Later among the works it cites.
Query expansion using contextual clue sampling with language models
Linqing Liu, Minghan Li, Jimmy Lin, Sebastian Riedel, and Pontus Stenetorp. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
RocketQA: An optimized training approach to dense passage retrieval for open-domain question answering
Yingqi Qu, Yuchen Ding, Jing Liu, Kai Liu, Ruiyang Ren, Wayne Xin Zhao, Daxiang Dong, Hua Wu, and Haifeng Wang. 2021 · 2021
Cited alongside, same era.
RocketQAv2: A joint training method for dense passage retrieval and passage re-ranking
Ruiyang Ren, Yingqi Qu, Jing Liu, Wayne Xin Zhao, QiaoQiao She, Hua Wu, Haifeng Wang, and Ji-Rong Wen. 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.
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
Cited alongside, same era.
Approximate nearest neighbor negative contrastive learning for dense text retrieval
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N. Bennett, Junaid Ahmed, and Arnold Overwijk. 2021 · 2021
Cited alongside, same era.
Overview of touché 2022: argument retrieval
Alexander Bondarenko, Maik Fröbe, Johannes Kiesel, Shahbaz Syed, Timon Gurcke, Meriem Beloucif, Alexander Panchenko, Chris Biemann, Benno Stein, Henning Wachsmuth, et al. 2022 · 2022
Cited alongside, same era.
From doc2query to doctttttquery
Rodrigo Nogueira and Jimmy Lin
Cited in the paper.
Measuring and narrowing the compositionality gap in language models
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis. 2022 · 2022
Later among the works it cites.
Recitation-augmented language models
Zhiqing Sun, Xuezhi Wang, Yi Tay, Yiming Yang, and Denny Zhou. 2022 · 2022
Later among the works it cites.
Text embeddings by weakly-supervised contrastive pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2022 · 2022
Later among the works it cites.
Generate rather than retrieve: Large language models are strong context generators
W. Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, and Meng Jiang. 2022 · 2022
Later among the works it cites.
Adversarial retriever-ranker for dense text retrieval
Hang Zhang, Yeyun Gong, Yelong Shen, Jiancheng Lv, Nan Duan, and Weizhu Chen. 2022 · 2022
Later among the works it cites.
OpenAI. 2023 · 2023
Closest in time.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aur’elien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
Closest in time.
SimLM: Pre-training with representation bottleneck for dense passage retrieval
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei. 2023 · 2023
Closest in time.