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Recent work in zero-shot listwise reranking using LLMs has achieved state-of-the-art results.
Generating Long Sequences with Sparse Transformers
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever. 2019 · 1904
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MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer. 2017 · 2017
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Overview of the TREC 2019 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M. Voorhees. 2019 · 2019
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Natural Questions: A Benchmark for Question Answering Research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur 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
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Latent Retrieval for Weakly Supervised Open Domain Question Answering
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Overview of the TREC 2020 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, and Daniel Campos. 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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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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Overview of the TREC 2021 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Jimmy Lin. 2021 · 2021
Cited alongside, same era.
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 · 2021
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Distilling Knowledge from Reader to Retriever for Question Answering
Gautier Izacard and Edouard Grave. 2021a · 2021
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Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
Gautier Izacard and Edouard Grave. 2021b · 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 · 2021
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Generation-Augmented Retrieval for Open-Domain Question Answering
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective
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Few-shot Learning with Retrieval Augmented Language Models
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Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
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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Scaling Up Models and Data with t5x
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Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents
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Yuning Mao, Pengcheng He, Xiaodong Liu, Yelong Shen, Jianfeng Gao, Jiawei Han, and Weizhu Chen. 2021 · 2021
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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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Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
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FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference
Michiel de Jong, Yury Zemlyanskiy, Joshua Ainslie, Nicholas FitzGerald, Sumit Sanghai, Fei Sha, and William Cohen. 2022 · 2022
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Fine-Tuning LLaMA for Multi-Stage Text Retrieval
Xueguang Ma, Liang Wang, Nan Yang, Furu Wei, and Jimmy Lin. 2023a
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Zero-Shot Listwise Document Reranking with a Large Language Model
Xueguang Ma, Xinyu Zhang, Ronak Pradeep, and Jimmy Lin. 2023b
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RankVicuna: Zero-Shot Listwise Document Reranking with Open-Source Large Language Models
Ronak Pradeep, Sahel Sharifymoghaddam, and Jimmy Lin. 2023a
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Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, and Zhaochun Ren. 2023 · 2023
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Pre-processing Matters! Improved Wikipedia Corpora for Open-Domain Question Answering
Manveer Singh Tamber, Ronak Pradeep, and Jimmy Lin. 2023 · 2023
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Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Models
Xinyu Zhang, Sebastian Hofstätter, Patrick Lewis, Raphael Tang, and Jimmy Lin. 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
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