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Zero-shot In-context learning is the phenomenon where models can perform the task simply given the instructions.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 1901
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.
Pharaoh: a beam search decoder for phrase-based statistical machine translation models
Philipp Koehn. 2004 · 2004
Earlier work this paper cites.
Decoding and diversity in machine translation
Nicholas Roberts, Davis Liang, Graham Neubig, and Zachary C Lipton. 2020 · 2011
Earlier work this paper cites.
A diversity-promoting objective function for neural conversation models
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and Bill Dolan. 2016 · 2016
Earlier work this paper cites.
Beam search strategies for neural machine translation
Markus Freitag and Yaser Al-Onaizan. 2017 · 2017
Earlier work this paper cites.
Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
Earlier work this paper cites.
A call for clarity in reporting BLEU scores
Matt Post. 2018 · 2018
Earlier work this paper cites.
Improved zero-shot neural machine translation via ignoring spurious correlations
Jiatao Gu, Yong Wang, Kyunghyun Cho, and Victor O.K. Li. 2019 · 2019
Earlier work this paper cites.
Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
Earlier work this paper cites.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020 · 2020
Earlier work this paper cites.
spacy: Industrial-strength natural language processing in python
Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Boyd Adriane. 2020 · 2020
Earlier work this paper cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
Earlier work this paper cites.
Decoding methods for neural narrative generation
Alexandra DeLucia, Aaron Mueller, Xiang Lisa Li, and João Sedoc. 2021 · 2021
Earlier work this paper cites.
Surface form competition: Why the highest probability answer isn’t always right
Ari Holtzman, Peter West, Vered Shwartz, Yejin Choi, and Luke Zettlemoyer. 2021 · 2021
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DExperts: Decoding-time controlled text generation with experts and anti-experts
Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, and Yejin Choi. 2021 · 2021
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Prompt programming for large language models: Beyond the few-shot paradigm
Laria Reynolds and Kyle McDonell. 2021 · 2021
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Decoding methods in neural language generation: A survey
Sina Zarrieß, Henrik Voigt, and Simeon Schüz. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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Robust MT evaluation with sentence-level multilingual augmentation
An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2022 · 2022
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Opt: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer. 2022 · 2022
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On the off-target problem of zero-shot multilingual neural machine translation
Liang Chen, Shuming Ma, Dongdong Zhang, Furu Wei, and Baobao Chang. 2023 · 2023
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Contrastive decoding: Open-ended text generation as optimization
Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis. 2023 · 2023
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Large language models as general pattern machines
Suvir Mirchandani, Fei Xia, Pete Florence, brian ichter, Danny Driess, Montserrat Gonzalez Arenas, Kanishka Rao, Dorsa Sadigh, and Andy Zeng. 2023 · 2023
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Duarte Alves, Ricardo Rei, Ana C Farinha, José G. C. de Souza, and André F. T. Martins. 2022 · 2022
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Results of WMT22 metrics shared task: Stop using BLEU – neural metrics are better and more robust
Markus Freitag, Ricardo Rei, Nitika Mathur, Chi-kiu Lo, Craig Stewart, Eleftherios Avramidis, Tom Kocmi, George Foster, Alon Lavie, and André F. T. Martins. 2022 · 2022
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The Flores-101 evaluation benchmark for low-resource and multilingual machine translation
Naman Goyal, Cynthia Gao, Vishrav Chaudhary, Peng-Jen Chen, Guillaume Wenzek, Da Ju, Sanjana Krishnan, Marc’Aurelio Ranzato, Francisco Guzmán, and Angela Fan. 2022 · 2022
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Answer-level calibration for free-form multiple choice question answering
Sawan Kumar. 2022 · 2022
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The bigscience roots corpus: A 1.6 tb composite multilingual dataset
Hugo Laurençon, Lucile Saulnier, Thomas Wang, Christopher Akiki, Albert Villanova del Moral, Teven Le Scao, Leandro Von Werra, Chenghao Mou, Eduardo González Ponferrada, Huu Nguyen, et al. 2022 · 2022
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Few-shot learning with multilingual generative language models
Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, and Xian Li. 2022 · 2022
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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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Yatin Nandwani, Vineet Kumar, Dinesh Raghu, Sachindra Joshi, and Luis Lastras. 2023 · 2023
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Trusting your evidence: Hallucinate less with context-aware decoding
Weijia Shi, Xiaochuang Han, Mike Lewis, Yulia Tsvetkov, Luke Zettlemoyer, and Scott Wen-tau Yih. 2023 · 2023
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Towards a better understanding of variations in zero-shot neural machine translation performance
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Xiaosu Wang, Yun Xiong, Beichen Kang, Yao Zhang, Philip S Yu, and Yangyong Zhu. 2023 · 2023
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Dola: Decoding by contrasting layers improves factuality in large language models
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Ebbs: An ensemble with bi-level beam search for zero-shot machine translation
Yuqiao Wen, Behzad Shayegh, Chenyang Huang, Yanshuai Cao, and Lili Mou. 2024 · 2024
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