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Listwise rerankers based on large language models (LLM) are the zero-shot state-of-the-art.
Rodrigo Nogueira and Kyunghyun Cho. 2019 · 1901
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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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Document ranking with a pretrained sequence-to-sequence model
Rodrigo Nogueira, Zhiying Jiang, and Jimmy Lin. 2020 · 2003
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Learning to rank: From pairwise approach to listwise approach
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, and Hang Li. 2007 · 2007
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The probabilistic relevance framework: BM25 and beyond
Stephen Robertson, Hugo Zaragoza, et al. 2009 · 2009
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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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Overview of the TREC 2020 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, and Daniel Campos. 2021 · 2020
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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
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Rethink training of bert rerankers in multi-stage retrieval pipeline
Luyu Gao, Zhuyun Dai, and Jamie Callan. 2021 · 2021
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Unsupervised dense information retrieval with contrastive learning
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. 2021 · 2021
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The expando-mono-duo design pattern for text ranking with pretrained sequence-to-sequence models
Ronak Pradeep, Rodrigo Nogueira, and Jimmy J. Lin. 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
Cited alongside, same era.
InPars: Unsupervised dataset generation for information retrieval
Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, and Rodrigo Nogueira. 2022 · 2022
Cited alongside, same era.
Promptagator: Few-shot dense retrieval from 8 examples
Zhuyun Dai, Vincent Y Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B Hall, and Ming-Wei Chang. 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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SGPT: GPT sentence embeddings for semantic search
Niklas Muennighoff. 2022 · 2022
QLoRA: Efficient finetuning of quantized LLMs
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023 · 2023
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InPars-v2: Large language models as efficient dataset generators for information retrieval
Vitor Jeronymo, Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, Roberto Lotufo, Jakub Zavrel, and Rodrigo Nogueira. 2023 · 2023
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Lost in the middle: How language models use long contexts
Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2023 · 2023
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RankVicuna: Zero-shot listwise document reranking with open-source large language models
Ronak Pradeep, Sahel Sharifymoghaddam, and Jimmy Lin. 2023 · 2023
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Squeezing water from a stone: A bag of tricks for further improving cross-encoder effectiveness for reranking
Ronak Pradeep, Yuqi Liu, Xinyu Zhang, Yilin Li, Andrew Yates, and Jimmy Lin. 2022 · 2022
Cited alongside, same era.
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
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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Vicuna: An open-source chatbot impressing GPT-4 with 90%* ChatGPT quality
Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E. Gonzalez, Ion Stoica, and Eric P. Xing. 2023 · 2023
Cited alongside, same era.
FlashAttention-2: Faster attention with better parallelism and work partitioning
Tri Dao. 2023 · 2023
Cited alongside, same era.
Fine-tuning LLaMA for multi-stage text retrieval
Xueguang Ma, Wang Liang, Nan Yang, Wei Furu, and Jimmy Lin. 2023a
Cited in the paper.
Zero-shot listwise document reranking with a large language model
Xueguang Ma, Xinyu Zhang, Ronak Pradeep, and Jimmy Lin. 2023b
Cited in the paper.
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang, et al. 2023 · 2023
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Code Llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al. 2023 · 2023
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Is ChatGPT good at search? Investigating large language models as re-ranking agent
Weiwei Sun, Lingyong Yan, Xinyu Ma, Pengjie Ren, Dawei Yin, and Zhaochun Ren. 2023 · 2023
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Found in the middle: Permutation self-consistency improves listwise ranking in large language models
Raphael Tang, Xinyu Zhang, Xueguang Ma, Jimmy Lin, and Ferhan Ture. 2023 · 2023
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Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric. P Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica. 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 Mike Bendersky. 2023 · 2023
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