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Neural Information Retrieval (NIR) has significantly improved upon heuristic-based Information Retrieval (IR) systems.
Sentence-bert: Sentence embeddings using siamese bert-networks
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Okapi at trec-3
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
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Support vector method for novelty detection
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To reject or not to reject: that is the question-an answer in case of neural classifiers
Claudio De Stefano, Carlo Sansone, and Mario Vento · 2000
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Support vector machines with embedded reject option
Giorgio Fumera and Fabio Roli · 2002
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Cumulated gain-based evaluation of ir techniques
Kalervo Järvelin and Jaana Kekäläinen · 2002
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Using tf-idf to determine word relevance in document queries
Juan Ramos et al · 2003
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Recall, precision and average precision
Mu Zhu · 2004
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Classification with a reject option using a hinge loss
Peter L Bartlett and Marten H Wegkamp · 2008
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The probabilistic relevance framework: Bm25 and beyond
Stephen Robertson, Hugo Zaragoza, et al · 2009
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Predicting partial orders: ranking with abstention
Weiwei Cheng, Michaël Rademaker, Bernard De Baets, and Eyke Hüllermeier · 2010
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On the foundations of noise-free selective classification
Ran El-Yaniv and Yair Wiener · 2010
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Practical variational inference for neural networks
Alex Graves · 2011
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Bayesian active learning for classification and preference learning
Neil Houlsby, Ferenc Huszár, Zoubin Ghahramani, and Máté Lengyel · 2011
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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On the feasibility of internet-scale author identification
Arvind Narayanan, Hristo Paskov, Neil Zhenqiang Gong, John Bethencourt, Emil Stefanov, Eui Chul Richard Shin, and Dawn Song · 2012
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The curse of dense low-dimensional information retrieval for large index sizes
Nils Reimers and Iryna Gurevych · 2012
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Pointwise tracking the optimal regression function
Yair Wiener and Ran El-Yaniv · 2012
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Semi-supervised question retrieval with gated convolutions
Tao Lei, Hrishikesh Joshi, Regina Barzilay, Tommi Jaakkola, Katerina Tymoshenko, Alessandro Moschitti, and Lluis Marquez · 2015
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Agnostic pointwise-competitive selective classification
Yair Wiener and Ran El-Yaniv · 2015
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Multi-factor duplicate question detection in stack overflow
Yun Zhang, David Lo, Xin Xia, and Jian-Ling Sun · 2015
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Boosting with abstention
Corinna Cortes, Giulia DeSalvo, and Mehryar Mohri · 2016
Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
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Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li · 2020
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A general framework for uncertainty estimation in deep learning
Antonio Loquercio, Mattia Segu, and Davide Scaramuzza · 2020
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mmarco: A multilingual version of ms marco passage ranking dataset, 2021
Luiz Henrique Bonifacio, Vitor Jeronymo, Hugo Queiroz Abonizio, Israel Campiotti, Marzieh Fadaee, , Roberto Lotufo, and Rodrigo Nogueira · 2021
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A statistical framework for efficient out of distribution detection in deep neural networks
Matan Haroush, Tzviel Frostig, Ruth Heller, and Daniel Soudry · 2021
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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A deep relevance matching model for ad-hoc retrieval
Jiafeng Guo, Yixing Fan, Qingyao Ai, and W Bruce Croft · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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Selective classification for deep neural networks
Yonatan Geifman and Ran El-Yaniv · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Datasets: A community library for natural language processing
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, and Thomas Wolf · 2021
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Large dual encoders are generalizable retrievers
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y Zhao, Yi Luan, Keith B Hall, Ming-Wei Chang, et al · 2021
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Revisiting mahalanobis distance for transformer-based out-of-domain detection
Alexander Podolskiy, Dmitry Lipin, Andrey Bout, Ekaterina Artemova, and Irina Piontkovskaya · 2021
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A simple fix to mahalanobis distance for improving near-ood detection
Jie Ren, Stanislav Fort, Jeremiah Liu, Abhijit Guha Roy, Shreyas Padhy, and Balaji Lakshminarayanan · 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
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The art of abstention: Selective prediction and error regularization for natural language processing
Ji Xin, Raphael Tang, Yaoliang Yu, and Jimmy Lin · 2021
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Modeling discriminative representations for out-of-domain detection with supervised contrastive learning
Zhiyuan Zeng, Keqing He, Yuanmeng Yan, Zijun Liu, Yanan Wu, Hong Xu, Huixing Jiang, and Weiran Xu · 2021
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Rainproof: An umbrella to shield text generators from out-of-distribution data
Maxime Darrin, Pablo Piantanida, and Pierre Colombo · 2022
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Igeood: An information geometry approach to out-of-distribution detection
Eduardo Dadalto Camara Gomes, Florence Alberge, Pierre Duhamel, and Pablo Piantanida · 2022
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Mteb: Massive text embedding benchmark
Niklas Muennighoff, Nouamane Tazi, Loïc Magne, and Nils Reimers · 2022
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Text and code embeddings by contrastive pre-training
Arvind Neelakantan, Tao Xu, Raul Puri, Alec Radford, Jesse Michael Han, Jerry Tworek, Qiming Yuan, Nikolas Tezak, Jong Wook Kim, Chris Hallacy, et al · 2022
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Text embeddings by weakly-supervised contrastive pre-training
Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, and Furu Wei · 2022
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Dense text retrieval based on pretrained language models: A survey
Wayne Xin Zhao, Jing Liu, Ruiyang Ren, and Ji-Rong Wen · 2022
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Knowledge-augmented language model verification
Jinheon Baek, Soyeong Jeong, Minki Kang, Jong C Park, and Sung Ju Hwang · 2023
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Antoine Lefebvre-Brossard, Stephane Gazaille, and Michel C Desmarais · 2023
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Anqi Mao, Mehryar Mohri, and Yutao Zhong · 2023
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Learning to filter context for retrieval-augmented generation
Zhiruo Wang, Jun Araki, Zhengbao Jiang, Md Rizwan Parvez, and Graham Neubig · 2023
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C-pack: Packaged resources to advance general chinese embedding, 2023
Shitao Xiao, Zheng Liu, Peitian Zhang, and Niklas Muennighoff · 2023
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Making retrieval-augmented language models robust to irrelevant context
Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant · 2023
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Retrieve anything to augment large language models, 2023
Peitian Zhang, Shitao Xiao, Zheng Liu, Zhicheng Dou, and Jian-Yun Nie · 2023
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Unsupervised layer-wise score aggregation for textual ood detection
Maxime Darrin, Guillaume Staerman, Eduardo Dadalto Câmara Gomes, Jackie CK Cheung, Pablo Piantanida, and Pierre Colombo · 2024
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Colpali: Efficient document retrieval with vision language models
Manuel Faysse, Hugues Sibille, Tony Wu, Gautier Viaud, Céline Hudelot, and Pierre Colombo · 2024
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Raft: Adapting language model to domain specific rag
Tianjun Zhang, Shishir G Patil, Naman Jain, Sheng Shen, Matei Zaharia, Ion Stoica, and Joseph E Gonzalez · 2024
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