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Listwise reranking with large language models (LLMs) enhances top-ranked results in retrieval-based applications.
Cumulated gain-based evaluation of ir techniques
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Yun Zhou and W Bruce Croft · 2007
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Adaptive computation time for recurrent neural networks
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Rodrigo Nogueira and Kyunghyun Cho · 2019
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Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
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Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M Voorhees · 2020
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Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych · 2021
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The expando-mono-duo design pattern for text ranking with pretrained sequence-to-sequence models
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Not all relevance scores are equal: Efficient uncertainty and calibration modeling for deep retrieval models
Daniel Cohen, Bhaskar Mitra, Oleg Lesota, Navid Rekabsaz, and Carsten Eickhoff · 2021
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How deep is your learning: the dl-hard annotated deep learning dataset
Iain Mackie, Jeffrey Dalton, and Andrew Yates · 2021
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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
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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
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Talebrush: Sketching stories with generative pretrained language models
John Joon Young Chung, Wooseok Kim, Kang Min Yoo, Hwaran Lee, Eytan Adar, and Minsuk Chang · 2022
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From distillation to hard negative sampling: Making sparse neural ir models more effective, 2022
Thibault Formal, Carlos Lassance, Benjamin Piwowarski, and Stéphane Clinchant · 2022
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Unsupervised dense information retrieval with contrastive learning, 2022
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave · 2022
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Large language models for information retrieval: A survey
Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Haonan Chen, Zheng Liu, Zhicheng Dou, and Ji-Rong Wen · 2023
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Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Haofen Wang, and Haofen Wang · 2023
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Fine-tuning llama for multi-stage text retrieval
Xueguang Ma, Liang Wang, Nan Yang, Furu Wei, and Jimmy Lin · 2024
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Beyond yes and no: Improving zero-shot llm rankers via scoring fine-grained relevance labels
Honglei Zhuang, Zhen Qin, Kai Hui, Junru Wu, Le Yan, Xuanhui Wang, and Michael Bendersky · 2024
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Prp-graph: Pairwise ranking prompting to llms with graph aggregation for effective text re-ranking
Jian Luo, Xuanang Chen, Ben He, and Le Sun · 2024
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Top-down partitioning for efficient list-wise ranking
Andrew Parry, Sean MacAvaney, and Debasis Ganguly · 2024
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Listt5: Listwise reranking with fusion-in-decoder improves zero-shot retrieval
Soyoung Yoon, Eunbi Choi, Jiyeon Kim, Hyeongu Yun, Yireun Kim, and Seung-won Hwang · 2024
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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
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Learning list-level domain-invariant representations for ranking, 2023
Ruicheng Xian, Honglei Zhuang, Zhen Qin, Hamed Zamani, Jing Lu, Ji Ma, Kai Hui, Han Zhao, Xuanhui Wang, and Michael Bendersky · 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
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Zero-shot listwise document reranking with a large language model
Xueguang Ma, Xinyu Zhang, Ronak Pradeep, and Jimmy Lin · 2023
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Parade: Passage ranking using demonstrations with llms
Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, et al · 2023
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Query-specific variable depth pooling via query performance prediction
Debasis Ganguly and Emine Yilmaz · 2023
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Joon Sung Park, Joseph C. O’Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, and Michael S. Bernstein · 2023
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Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal · 2023
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Wenhan Liu, Yutao Zhu, and Zhicheng Dou · 2024
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Query performance prediction: From fundamentals to advanced techniques
Negar Arabzadeh, Chuan Meng, Mohammad Aliannejadi, and Ebrahim Bagheri · 2024
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Omar Shaikh, Valentino Chai, Michele J. Gelfand, Diyi Yang, and Michael S. Bernstein · 2024
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Rar-b: Reasoning as retrieval benchmark
Chenghao Xiao, G Thomas Hudson, and Noura Al Moubayed · 2024
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Reasoningrank: Teaching student models to rank through reasoning-based knowledge distillation
Yuelyu Ji, Zhuochun Li, Rui Meng, and Daqing He · 2024
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Judgerank: Leveraging large language models for reasoning-intensive reranking
Tong Niu, Shafiq Joty, Ye Liu, Caiming Xiong, Yingbo Zhou, and Semih Yavuz · 2024
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Budget-constrained document re-ranking with bayesian LM-based pairwise comparisons
Anonymous · 2025
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Llama 3.3 70B Instruct model card
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Rank1: Test-time compute for reranking in information retrieval
Orion Weller, Kathryn Ricci, Eugene Yang, Andrew Yates, Dawn Lawrie, and Benjamin Van Durme · 2025
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Rank-r1: Enhancing reasoning in llm-based document rerankers via reinforcement learning
Shengyao Zhuang, Xueguang Ma, Bevan Koopman, Jimmy Lin, and Guido Zuccon · 2025
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