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We tested over 20 Transformer models for ranking long documents (including recent LongP models trained with FlashAttention and RankGPT models "powered" by OpenAI and Anthropic cloud APIs).
Rodrigo Nogueira and Kyunghyun Cho. 2019 · 1901
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Document expansion by query prediction
Rodrigo Nogueira, Wei Yang, Jimmy Lin, and Kyunghyun Cho. 2019 · 1904
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Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2019 · 1910
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The mathematics of statistical machine translation: Parameter estimation
Peter F. Brown, Stephen Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. 1993 · 1993
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Information retrieval as statistical translation
Adam Berger and John Lafferty. 1999 · 1999
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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. 2020 · 2003
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Longformer: The long-document transformer
Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020 · 2004
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Overview of the TREC 2004 terabyte track
Charles L. A. Clarke, Nick Craswell, and Ian Soboroff. 2004 · 2004
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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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Understanding inverse document frequency: on theoretical arguments for IDF
Stephen Robertson. 2004 · 2004
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Overview of the trec 2004 robust retrieval track
Ellen Voorhees. 2004 · 2004
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On the stability of fine-tuning BERT: misconceptions, explanations, and strong baselines
Marius Mosbach, Maksym Andriushchenko, and Dietrich Klakow. 2020 · 2006
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Argumentative feedback: A linguistically-motivated term expansion for information retrieval
Patrick Ruch, Imad Tbahriti, Julien Gobeill, and Alan R. Aronson. 2006 · 2006
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Million query track 2008 overview
James Allan, Javed A. Aslam, Virgil Pavlu, Evangelos Kanoulas, and Ben Carterette. 2008 · 2008
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Efficient transformers: A survey
Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler. 2020 · 2009
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Natural language processing (almost) from scratch
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011 · 2011
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Engineering efficient and effective non-metric space library
Leonid Boytsov and Bilegsaikhan Naidan. 2013 · 2013
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TREC 2013 web track overview
Kevyn Collins-Thompson, Paul N. Bennett, Fernando Diaz, Charlie Clarke, and Ellen M. Voorhees. 2013a · 2013
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TREC 2013 web track overview
Kevyn Collins-Thompson, Paul N. Bennett, Fernando Diaz, Charlie Clarke, and Ellen M. Voorhees. 2013b · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Gregory S. Corrado, and Jeffrey Dean. 2013 · 2013
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A comparison of retrieval models using term dependencies
Samuel Huston and W Bruce Croft. 2014 · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
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MS MARCO: A human generated machine reading comprehension dataset
Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, et al. 2016 · 2016
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A full-text learning to rank dataset for medical information retrieval
Vera Boteva, Demian Gholipour, Artem Sokolov, and Stefan Riezler. 2016 · 2016
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A deep relevance matching model for ad-hoc retrieval
Jiafeng Guo, Yixing Fan, Qingyao Ai, and W. Bruce Croft. 2016 · 2016
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Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016 · 2016
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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
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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End-to-end neural ad-hoc ranking with kernel pooling
Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, and Russell Power. 2017 · 2017
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Co-pacrr: A context-aware neural IR model for ad-hoc retrieval
Kai Hui, Andrew Yates, Klaus Berberich, and Gerard de Melo. 2018 · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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The annotated transformer
Alexander M Rush. 2018 · 2018
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Deeper text understanding for IR with contextual neural language modeling
Zhuyun Dai and Jamie Callan. 2019 · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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A deep look into neural ranking models for information retrieval
Jiafeng Guo, Yixing Fan, Liang Pang, Liu Yang, Qingyao Ai, Hamed Zamani, Chen Wu, W Bruce Croft, and Xueqi Cheng. 2019 · 2019
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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Leonid Boytsov, Tianyi Lin, Fangwei Gao, Yutian Zhao, Jeffrey Huang, and Eric Nyberg. 2022 · 2022
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Overview of the TREC 2022 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, Jimmy Lin, Ellen M. Voorhees, and Ian Soboroff. 2022 · 2022
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Flashattention: Fast and memory-efficient exact attention with io-awareness
Tri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra, and Christopher Ré. 2022 · 2022
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Leveraging multi-view inter-passage interactions for neural document ranking
Chengzhen Fu, Enrui Hu, Letian Feng, Zhicheng Dou, Yantao Jia, Lei Chen, Fan Yu, and Zhao Cao. 2022 · 2022
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Long document re-ranking with modular re-ranker
Luyu Gao and Jamie Callan. 2022 · 2022
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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The neural hype and comparisons against weak baselines
Jimmy Lin. 2019 · 2019
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CEDR: contextualized embeddings for document ranking
Sean MacAvaney, Andrew Yates, Arman Cohan, and Nazli Goharian. 2019 · 2019
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From doc2query to docTTTTTquery
Rodrigo Nogueira and Jimmy Lin. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
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Applying BERT to document retrieval with birch
Zeynep Akkalyoncu Yilmaz, Shengjin Wang, Wei Yang, Haotian Zhang, and Jimmy Lin. 2019 · 2019
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Overview of touché 2020: Argument retrieval - extended abstract
Alexander Bondarenko, Maik Fröbe, Meriem Beloucif, Lukas Gienapp, Yamen Ajjour, Alexander Panchenko, Chris Biemann, Benno Stein, Henning Wachsmuth, Martin Potthast, and Matthias Hagen. 2020 · 2020
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LongT5: Efficient text-to-text transformer for long sequences
Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, and Yinfei Yang. 2022 · 2022
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ABNIRML: analyzing the behavior of neural IR models
Sean MacAvaney, Sergey Feldman, Nazli Goharian, Doug Downey, and Arman Cohan. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
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Evaluating the robustness of retrieval pipelines with query variation generators
Gustavo Penha, Arthur Câmara, and Claudia Hauff. 2022 · 2022
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Train short, test long: Attention with linear biases enables input length extrapolation
Ofir Press, Noah Smith, and Mike Lewis. 2022 · 2022
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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. 2022 · 2022
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Overview of the TREC 2023 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Hossein A. Rahmani, Daniel Campos, Jimmy Lin, Ellen M. Voorhees, and Ian Soboroff. 2023 · 2023
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Jina embeddings 2: 8192-token general-purpose text embeddings for long documents
Michael Günther, Jackmin Ong, Isabelle Mohr, Alaeddine Abdessalem, Tanguy Abel, Mohammad Kalim Akram, Susana Guzman, Georgios Mastrapas, Saba Sturua, Bo Wang, Maximilian Werk, Nan Wang, and Han Xiao. 2023 · 2023
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Fine-tuning llama for multi-stage text retrieval
Xueguang Ma, Liang Wang, Nan Yang, Furu Wei, and Jimmy Lin. 2023 · 2023
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OpenAI. 2023 · 2023
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MosaicBERT: A bidirectional encoder optimized for fast pretraining
Jacob Portes, Alexander R Trott, Sam Havens, DANIEL KING, Abhinav Venigalla, Moin Nadeem, Nikhil Sardana, Daya Khudia, and Jonathan Frankle. 2023 · 2023
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Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu. 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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Make your LLM fully utilize the context
Shengnan An, Zexiong Ma, Zeqi Lin, Nanning Zheng, Jian-Guang Lou, and Weizhu Chen. 2024 · 2024
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The claude 3 model family: Opus, sonnet, haiku
Anthropic. 2024 · 2024
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Dwell in the beginning: How language models embed long documents for dense retrieval
João Coelho, Bruno Martins, João Magalhães, Jamie Callan, and Chenyan Xiong. 2024 · 2024
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Found in the middle: Calibrating positional attention bias improves long context utilization
Cheng-Yu Hsieh, Yung-Sung Chuang, Chun-Liang Li, Zifeng Wang, Long T. Le, Abhishek Kumar, James R. Glass, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, and Tomas Pfister. 2024 · 2024
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PARADE: passage representation aggregation for document reranking
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Lost in the middle: How language models use long contexts
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Revisiting bag of words document representations for efficient ranking with transformers
David Rau, Mostafa Dehghani, and Jaap Kamps. 2024 · 2024
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Benchmarking and building long-context retrieval models with loco and M2-BERT
Jon Saad-Falcon, Daniel Y. Fu, Simran Arora, Neel Guha, and Christopher Ré. 2024 · 2024
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A Systematic Evaluation of Transfer Learning and Pseudo-Labeling with BERT-Based Ranking Models , page 2081–2085. Association for Computing Machinery, New York, NY, USA
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