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Ranking documents using Large Language Models (LLMs) by directly feeding the query and candidate documents into the prompt is an interesting and practical 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. 2020 · 1901
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Rodrigo Nogueira and Kyunghyun Cho. 2019 · 1901
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Aggregating inconsistent information: ranking and clustering
Nir Ailon, Moses Charikar, and Alantha Newman. 2008 · 2008
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Learning to rank for information retrieval
Tie-Yan Liu. 2009 · 2009
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
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Simple, robust and optimal ranking from pairwise comparisons
Nihar B Shah and Martin J Wainwright. 2018 · 2018
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Retrieval of the best counterargument without prior topic knowledge
Henning Wachsmuth, Shahbaz Syed, and Benno Stein. 2018 · 2018
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Calibration of pre-trained transformers
Shrey Desai and Greg Durrett. 2020 · 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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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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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. 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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Large language models are zero-shot clinical information extractors
Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David Sontag. 2022 · 2022
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InPars: Unsupervised dataset generation for information retrieval
Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, and Rodrigo Nogueira. 2022 · 2022
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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 · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 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
Cited alongside, same era.
Precise zero-shot dense retrieval without relevance labels
Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. 2022 · 2022
Cited alongside, same era.
Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch. 2022 · 2022
Cited alongside, same era.
Large language models are zero-shot rankers for recommender systems
Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian McAuley, and Wayne Xin Zhao. 2023 · 2023
Closest in time.
Query expansion by prompting large language models
Rolf Jagerman, Honglei Zhuang, Zhen Qin, Xuanhui Wang, and Michael Bendersky. 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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One-shot labeling for automatic relevance estimation
Sean MacAvaney and Luca Soldaini. 2023 · 2023
Closest in time.
OpenAI. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Cited alongside, same era.
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. 2022 · 2022
Cited alongside, same era.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022 · 2022
Cited alongside, same era.
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
Cited alongside, same era.
Improving passage retrieval with zero-shot question generation
Devendra Singh Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
A neural corpus indexer for document retrieval
Yujing Wang, Yingyan Hou, Haonan Wang, Ziming Miao, Shibin Wu, Qi Chen, Yuqing Xia, Chengmin Chi, Guoshuai Zhao, Zheng Liu, et al. 2022 · 2022
Cited alongside, same era.
Task-aware retrieval with instructions
Akari Asai, Timo Schick, Patrick Lewis, Xilun Chen, Gautier Izacard, Sebastian Riedel, Hannaneh Hajishirzi, and Wen-tau Yih. 2023 · 2023
Cited alongside, same era.
Xingjian Bai and Christian Coester. 2023 · 2023
Cited alongside, same era.
Closest in time.
Rankvicuna: Zero-shot listwise document reranking with open-source large language models
Ronak Pradeep, Sahel Sharifymoghaddam, and Jimmy Lin. 2023 · 2023
Closest in time.
Rd-suite: A benchmark for ranking distillation
Zhen Qin, Rolf Jagerman, Rama Kumar Pasumarthi, Honglei Zhuang, He Zhang, Aijun Bai, Kai Hui, Le Yan, and Xuanhui Wang. 2023 · 2023
Closest in time.
Stanford Alpaca: An instruction-following LLaMA model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
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GPT-3.5 Turbo vs GPT-4: What’s the difference?
Adam VanBuskirk. 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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On what basis? predicting text preference via structured comparative reasoning
Jing Nathan Yan, Tianqi Liu, Justin T Chiu, Jiaming Shen, Zhen Qin, Yue Yu, Yao Zhao, Charu Lakshmanan, Yair Kurzion, Alexander M Rush, et al. 2023 · 2023
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Large language models for information retrieval: A survey
Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Zhicheng Dou, and Ji-Rong Wen. 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
Closest in time.
Lipo: Listwise preference optimization through learning-to-rank
Tianqi Liu, Zhen Qin, Junru Wu, Jiaming Shen, Misha Khalman, Rishabh Joshi, Yao Zhao, Mohammad Saleh, Simon Baumgartner, Jialu Liu, et al. 2024 · 2024
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Llm comparative assessment: Zero-shot nlg evaluation through pairwise comparisons using large language models
Adian Liusie, Potsawee Manakul, and Mark Gales. 2024 · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2024 · 2024
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