Fetching the paper…
Reading the bibliography…
Recent studies show that large language models (LLMs) can be instructed to effectively perform zero-shot passage re-ranking, in which the results of a first stage retrieval method, such as BM25, are rated and reordered to improve relevance.
Committee-based sampling for training probabilistic classifiers
Ido Dagan and Shlomo Engelson Argamon. 1995 · 1995
Earlier work this paper cites.
A Language Modeling Approach to Information Retrieval
Jay M. Ponte and W. Bruce Croft. 1998 · 1998
Earlier work this paper cites.
Toward optimal active learning through monte carlo estimation of error reduction
Nicholas Roy and Andrew McCallum. 2001 · 2001
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2005
Earlier work this paper cites.
Active learning literature survey
Burr Settles. 2009 · 2009
Earlier work this paper cites.
Training region-based object detectors with online hard example mining
Abhinav Shrivastava, Abhinav Kumar Gupta, and Ross B. Girshick. 2016 · 2016
Earlier work this paper cites.
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples
Haw-Shiuan Chang, Erik G. Learned-Miller, and Andrew McCallum. 2017 · 2017
Earlier work this paper cites.
Universal Sentence Encoder for English
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, and Ray Kurzweil. 2018 · 2018
Earlier work this paper cites.
From doc2query to docTTTTTquery
Rodrigo Nogueira, Jimmy Lin, and AI Epistemic. 2019 · 2019
Earlier work this paper cites.
Beyond [CLS] through ranking by generation
Cicero Nogueira dos Santos, Xiaofei Ma, Ramesh Nallapati, Zhiheng Huang, and Bing Xiang. 2020 · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Zero-shot neural passage retrieval via domain-targeted synthetic question generation
Ji Ma, Ivan Korotkov, Yinfei Yang, Keith Hall, and Ryan McDonald. 2021 · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
InPars: Unsupervised dataset generation for information retrieval
Luiz Bonifacio, Hugo Abonizio, Marzieh Fadaee, and Rodrigo Nogueira. 2022 · 2022
Earlier work this paper cites.
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
Cited alongside, same era.
ED2LM: Encoder-decoder to language model for faster document re-ranking inference
Kai Hui, Honglei Zhuang, Tao Chen, Zhen Qin, Jing Lu, Dara Bahri, Ji Ma, Jai Gupta, Cicero Nogueira dos Santos, Yi Tay, and Donald Metzler. 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.
MetaICL: Learning to learn in context
Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022 · 2022
Cited alongside, same era.
Compositional semantic parsing with large language models
Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, and Denny Zhou. 2023 · 2023
Closest in time.
Precise zero-shot dense retrieval without relevance labels
Luyu Gao, Xueguang Ma, Jimmy Lin, and Jamie Callan. 2023 · 2023
Closest in time.
Rohan Anil Google, Andrew M. Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, Eric Chu, Jonathan H. Clark, Laurent El Shafey, Yanping Huang, Kathy Meier-Hellstern, Gaurav Mishra, Erica Moreira, Mark Omernick, Kevin Robinson, Sebastian Ruder, Yi Tay, Kefan Xiao, Yuanzhong Xu, Yujing Zhang, Gustavo Hernandez Abrego, Junwhan Ahn, Jacob Austin, Paul Barham, Jan Botha, James Bradbury, Siddhartha Brahma, Kevin Brooks, Michele Catasta, Yong Cheng, Colin Cherry, Christopher A. Choquette-Choo, Aakanksha Chowdhery, Clément Crepy, Shachi Dave, Mostafa Dehghani, Sunipa Dev, Jacob Devlin, Mark Díaz, Nan Du, Ethan Dyer, Vlad Feinberg, Fangxiaoyu Feng, Vlad Fienber, Markus Freitag, Xavier Garcia, Sebastian Gehrmann, Lucas Gonzalez, Guy Gur-Ari, Steven Hand, Hadi Hashemi, Le Hou, Joshua Howland, Andrea Hu, Jeffrey Hui, Jeremy Hurwitz, Michael Isard, Abe Ittycheriah, Matthew Jagielski, Wenhao Jia, Kathleen Kenealy, Maxim Krikun, Sneha Kudugunta, Chang Lan, Katherine Lee, Benjamin Lee, Eric Li, Music Li, Wei Li, YaGuang Li, Jian Li, Hyeontaek Lim, Hanzhao Lin, Zhongtao Liu, Frederick Liu, Marcello Maggioni, Aroma Mahendru, Joshua Maynez, Vedant Misra, Maysam Moussalem, Zachary Nado, John Nham, Eric Ni, Andrew Nystrom, Alicia Parrish, Marie Pellat, Martin Polacek, Alex Polozov, Reiner Pope, Siyuan Qiao, Emily Reif, Bryan Richter, Parker Riley, Alex Castro Ros, Aurko Roy, Brennan Saeta, Rajkumar Samuel, Renee Shelby, Ambrose Slone, Daniel Smilkov, David R. So, Daniel Sohn, Simon Tokumine, Dasha Valter, Vijay Vasudevan, Kiran Vodrahalli, Xuezhi Wang, Pidong Wang, Zirui Wang, Tao Wang, John Wieting, Yuhuai Wu, Kelvin Xu, Yunhan Xu, Linting Xue, Pengcheng Yin, Jiahui Yu, Qiao Zhang, Steven Zheng, Ce Zheng, Weikang Zhou, Denny Zhou, Slav Petrov, and Yonghui Wu. 2023 · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 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.
Efficient nearest neighbor search for cross-encoder models using matrix factorization
Nishant Yadav, Nicholas Monath, Rico Angell, Manzil Zaheer, and Andrew McCallum. 2022 · 2022
Cited alongside, same era.
Active example selection for in-context learning
Yiming Zhang, Shi Feng, and Chenhao Tan. 2022 · 2022
Cited alongside, same era.
In-context examples selection for machine translation
Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer, and Marjan Ghazvininejad. 2023 · 2023
Cited alongside, same era.
What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou. 2023 · 2023
Cited alongside, same era.
A Statistical Perspective on Retrieval-Based Models
Soumya Basu, Ankit Singh Rawat, and Manzil Zaheer. 2023 · 2023
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 Hall, and Ming-Wei Chang. 2023 · 2023
Cited alongside, same era.
Closest in time.
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
Closest in time.
Few-shot Reranking for Multi-hop QA via Language Model Prompting
Muhammad Khalifa, Lajanugen Logeswaran, Moontae Lee, Honglak Lee, and Lu Wang. 2023 · 2023
Closest in time.
Diverse demonstrations improve in-context compositional generalization
Itay Levy, Ben Bogin, and Jonathan Berant. 2023 · 2023
Closest in time.
Finding support examples for in-context learning
Xiaonan Li and Xipeng Qiu. 2023 · 2023
Closest in time.
Dr.ICL: Demonstration-Retrieved In-context Learning
Man Luo, Xin Xu, Zhuyun Dai, Panupong Pasupat, Mehran Kazemi, Chitta Baral, Vaiva Imbrasaite, and Vincent Zhao. 2023 · 2023
Closest in time.
RankVicuna: Zero-Shot Listwise Document Reranking with a Large Language Model
Xueguang Ma, Xinyu Crystina Zhang, Ronak Pradeep, and Jimmy Lin. 2023 · 2023
Closest in time.
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
Closest in time.
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
Closest in time.
Compositional exemplars for in-context learning
Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Tao Yu, and Lingpeng Kong. 2023 · 2023
Closest in time.
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
Closest in time.