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Neural autoregressive sequence models are used to generate sequences in a variety of natural language processing (NLP) tasks, where they are evaluated according to sequence-level task losses.
The curious case of neural text degeneration
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Rush, A. M.; Chopra, S.; and Weston, J. 2015 · 2015
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Vinyals, O.; Quoc, G.; and Le, V. 2015 · 2015
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Andor, D.; Alberti, C.; Weiss, D.; Severyn, A.; Presta, A.; Ganchev, K.; Petrov, S.; and Collins, M. 2016 · 2016
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Stahlberg, F.; and Byrne, B. 2019 · 2019
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Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective
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Zellers, R.; Holtzman, A.; Rashkin, H.; Bisk, Y.; Farhadi, A.; Roesner, F.; and Choi, Y. 2019 · 2019
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Language Models are Few-Shot Learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020 · 2020
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Caccia, M.; Caccia, L.; Fedus, W.; Larochelle Google Brain, H.; Mila, M.; Research, F. A.; and Canada CIFAR Chair, M. A. 2020 · 2020
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