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Minimum Bayes-Risk (MBR) decoding is shown to be a powerful alternative to beam search decoding for a wide range of text generation tasks.
Differential metrics in probability spaces
C Radakrishna Rao. 1987 · 1987
Earlier work this paper cites.
Clustering by means of medoids
LKPJ Rdusseeun and P Kaufman. 1987 · 1987
Earlier work this paper cites.
Partitioning Around Medoids (Program PAM) , chapter 2. John Wiley & Sons, Ltd
Leonard Kaufman and Peter J. Rousseeuw. 1990 · 1990
Earlier work this paper cites.
Parsing algorithms and metrics
Joshua Goodman. 1996 · 1996
Earlier work this paper cites.
Minimum bayes-risk automatic speech recognition
Vaibhava Goel and William J Byrne. 2000 · 2000
Earlier work this paper cites.
Minimum Bayes-risk word alignments of bilingual texts
Shankar Kumar and William Byrne. 2002 · 2002
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
Earlier work this paper cites.
Minimum Bayes-risk decoding for statistical machine translation
Shankar Kumar and William Byrne. 2004 · 2004
Earlier work this paper cites.
Fast approximation of centrality
David Eppstein and Joseph Wang. 2006 · 2006
Earlier work this paper cites.
Ranking of closeness centrality for large-scale social networks
Kazuya Okamoto, Wei Chen, and Xiang-Yang Li. 2008 · 2008
Earlier work this paper cites.
Sequence transduction with recurrent neural networks
Alex Graves. 2012 · 2012
Earlier work this paper cites.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014 · 2014
Earlier work this paper cites.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
Earlier work this paper cites.
A neural attention model for abstractive sentence summarization
Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Earlier work this paper cites.
Tight (lower) bounds for the fixed budget best arm identification bandit problem
Alexandra Carpentier and Andrea Locatelli. 2016 · 2016
Earlier work this paper cites.
On the complexity of best-arm identification in multi-armed bandit models
Emilie Kaufmann, Olivier Cappé, and Aurélien Garivier. 2016 · 2016
Earlier work this paper cites.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Łukasz Kaiser, Stephan Gouws, Yoshikiyo Kato, Taku Kudo, Hideto Kazawa, Keith Stevens, George Kurian, Nishant Patil, Wei Wang, Cliff Young, Jason Smith, Jason Riesa, Alex Rudnick, Oriol Vinyals, Greg Corrado, Macduff Hughes, and Jeffrey Dean. 2016 · 2016
Earlier work this paper cites.
Guided open vocabulary image captioning with constrained beam search
Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 2017 · 2017
Earlier work this paper cites.
Medoids in almost-linear time via multi-armed bandits
Vivek Bagaria, Govinda Kamath, Vasilis Ntranos, Martin Zhang, and David Tse. 2018 · 2018
Earlier work this paper cites.
What doubling tricks can and can’t do for multi-armed bandits
Lilian Besson and Emilie Kaufmann. 2018 · 2018
Earlier work this paper cites.
Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
Earlier work this paper cites.
Ultra fast medoid identification via correlated sequential halving
Tavor Baharav and David Tse. 2019 · 2019
Earlier work this paper cites.
Empirical analysis of beam search performance degradation in neural sequence models
Eldan Cohen and Christopher Beck. 2019 · 2019
Earlier work this paper cites.
SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019 · 2019
Cited alongside, same era.
fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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On NMT search errors and model errors: Cat got your tongue?
Felix Stahlberg and Bill Byrne. 2019 · 2019
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Is MAP decoding all you need? the inadequacy of the mode in neural machine translation
Bryan Eikema and Wilker Aziz. 2020 · 2020
Cited alongside, same era.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
Understanding dataset difficulty with 𝒱 \mathcal{V} -usable information
Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta. 2022 · 2022
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Quality-aware decoding for neural machine translation
Patrick Fernandes, António Farinhas, Ricardo Rei, José G. C. de Souza, Perez Ogayo, Graham Neubig, and Andre Martins. 2022 · 2022
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High quality rather than high model probability: Minimum Bayes risk decoding with neural metrics
Markus Freitag, David Grangier, Qijun Tan, and Bowen Liang. 2022 · 2022
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Truncation sampling as language model desmoothing
John Hewitt, Christopher Manning, and Percy Liang. 2022 · 2022
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Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman. 2022 · 2022
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
If beam search is the answer, what was the question?
Clara Meister, Ryan Cotterell, and Tim Vieira. 2020 · 2020
Cited alongside, same era.
Unbabel’s participation in the WMT20 metrics shared task
Ricardo Rei, Craig Stewart, Ana C Farinha, and Alon Lavie. 2020 · 2020
Cited alongside, same era.
BLEURT: Learning robust metrics for text generation
Thibault Sellam, Dipanjan Das, and Ankur Parikh. 2020 · 2020
Cited alongside, same era.
Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020 · 2020
Cited alongside, same era.
Consistency of a recurrent language model with respect to incomplete decoding
Sean Welleck, Ilia Kulikov, Jaedeok Kim, Richard Yuanzhe Pang, and Kyunghyun Cho. 2020 · 2020
Cited alongside, same era.
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COMET-22: Unbabel-IST 2022 submission for the metrics shared task
Ricardo Rei, José G. C. de Souza, Duarte Alves, Chrysoula Zerva, Ana C Farinha, Taisiya Glushkova, Alon Lavie, Luisa Coheur, and André F. T. Martins. 2022 · 2022
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It’s MBR all the way down: Modern generation techniques through the lens of minimum Bayes risk
Amanda Bertsch, Alex Xie, Graham Neubig, and Matthew Gormley. 2023 · 2023
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Faster minimum Bayes risk decoding with confidence-based pruning
Julius Cheng and Andreas Vlachos. 2023 · 2023
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Ultrafeedback: Boosting language models with high-quality feedback
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun. 2023 · 2023
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An empirical study of translation hypothesis ensembling with large language models
António Farinhas, José de Souza, and Andre Martins. 2023 · 2023
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Towards efficient minimum bayes risk decoding
Gerson Foks. 2023 · 2023
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Epsilon sampling rocks: Investigating sampling strategies for minimum Bayes risk decoding for machine translation
Markus Freitag, Behrooz Ghorbani, and Patrick Fernandes. 2023 · 2023
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
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MetricX-23: The Google submission to the WMT 2023 metrics shared task
Juraj Juraska, Mara Finkelstein, Daniel Deutsch, Aditya Siddhant, Mehdi Mirzazadeh, and Markus Freitag. 2023 · 2023
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Openassistant conversations - democratizing large language model alignment
Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi Rui Tam, Keith Stevens, Abdullah Barhoum, Duc Minh Nguyen, Oliver Stanley, Richárd Nagyfi, Shahul ES, Sameer Suri, David Alexandrovich Glushkov, Arnav Varma Dantuluri, Andrew Maguire, Christoph Schuhmann, Huu Nguyen, and Alexander Julian Mattick. 2023 · 2023
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
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Direct preference optimization for neural machine translation with minimum bayes risk decoding
Guangyu Yang, Jinghong Chen, Weizhe Lin, and Bill Byrne. 2023 · 2023
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MBR and QE finetuning: Training-time distillation of the best and most expensive decoding methods
Mara Finkelstein and Markus Freitag. 2024 · 2024
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Model-based minimum bayes risk decoding
Yuu Jinnai, Tetsuro Morimura, Ukyo Honda, Kaito Ariu, and Kenshi Abe. 2024 · 2024
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Junyou Li, Qin Zhang, Yangbin Yu, Qiang Fu, and Deheng Ye. 2024 · 2024
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Linear-time minimum bayes risk decoding with reference aggregation
Jannis Vamvas and Rico Sennrich. 2024 · 2024
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