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Reranking documents based on their relevance to a given query is a critical task in information retrieval.
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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 Lin. 2021 · 2021
Cited alongside, same era.
Beir: A heterogenous 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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On the generalizability and predictability of recommender systems
Duncan McElfresh, Sujay Khandagale, Jonathan Valverde, John Dickerson, and Colin White. 2022 · 2022
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Experiments on generalizability of user-oriented fairness in recommender systems. In Proceedings of the 45th International ACM SIGIR Conference on research and development in information retrieval . 2755–2764
Hossein A Rahmani, Mohammadmehdi Naghiaei, Mahdi Dehghan, and Mohammad Aliannejadi. 2022 · 2022
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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Beyond yes and no: Improving zero-shot llm rankers via scoring fine-grained relevance labels
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RankingSHAP–Listwise Feature Attribution Explanations for Ranking Models
Maria Heuss, Maarten de Rijke, and Avishek Anand. 2024 · 2024
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TriSum: Learning Summarization Ability from Large Language Models with Structured Rationale
Pengcheng Jiang, Cao Xiao, Zifeng Wang, Parminder Bhatia, Jimeng Sun, and Jiawei Han. 2024 · 2024
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Hongjin Su, Weijia Shi, Jungo Kasai, Yizhong Wang, Yushi Hu, Mari Ostendorf, Wen-tau Yih, Noah A Smith, Luke Zettlemoyer, and Tao Yu. 2022 · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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One-shot labeling for automatic relevance estimation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval . 2230–2235
Sean MacAvaney and Luca Soldaini. 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. 2023a · 2023
Cited alongside, same era.
RankZephyr: Effective and Robust Zero-Shot Listwise Reranking is a Breeze!
Ronak Pradeep, Sahel Sharifymoghaddam, and Jimmy Lin. 2023b · 2023
Cited alongside, same era.
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
Cited alongside, same era.
The Claude 3 Model Family: Opus, Sonnet, Haiku
[n. d.]
Cited in the paper.
Can Jin, Hongwu Peng, Shiyu Zhao, Zhenting Wang, Wujiang Xu, Ligong Han, Jiahui Zhao, Kai Zhong, Sanguthevar Rajasekaran, and Dimitris N Metaxas. 2024 · 2024
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Leveraging Passage Embeddings for Efficient Listwise Reranking with Large Language Models
Qi Liu, Bo Wang, Nan Wang, and Jiaxin Mao. 2024 · 2024
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Few-shot Prompting for Pairwise Ranking: An Effective Non-Parametric Retrieval Model. In Findings of the Association for Computational Linguistics: EMNLP 2024 , Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (Eds.). Association for Computational Linguistics, Miami, Florida, USA, 12363–12377
Nilanjan Sinhababu, Andrew Parry, Debasis Ganguly, Debasis Samanta, and Pabitra Mitra. 2024 · 2024
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BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval
Hongjin Su, Howard Yen, Mengzhou Xia, Weijia Shi, Niklas Muennighoff, Han-yu Wang, Haisu Liu, Quan Shi, Zachary S Siegel, Michael Tang, et al · 2024
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Puxuan Yu, Daniel Cohen, Hemank Lamba, Joel Tetreault, and Alex Jaimes. 2024 · 2024
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