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Given a prefix (context), open-ended generation aims to decode texts that are coherent, which do not abruptly drift from previous topics, and informative, which do not suffer from undesired repetitions.
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.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 1904
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Neural text generation with unlikelihood training
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2019 · 1908
Earlier work this paper cites.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
Earlier work this paper cites.
Towards a human-like open-domain chatbot
Daniel Adiwardana, Minh-Thang Luong, David R So, Jamie Hall, Noah Fiedel, Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu, et al. 2020 · 2001
Earlier work this paper cites.
Is map decoding all you need? the inadequacy of the mode in neural machine translation
Bryan Eikema and Wilker Aziz. 2020 · 2005
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Pointer sentinel mixture models
Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2016 · 2016
Earlier work this paper cites.
Hierarchical neural story generation
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Earlier work this paper cites.
Breaking the beam search curse: A study of (re-)scoring methods and stopping criteria for neural machine translation
Yilin Yang, Liang Huang, and Mingbo Ma. 2018 · 2018
Earlier work this paper cites.
Towards coherent and cohesive long-form text generation
Woon Sang Cho, Pengchuan Zhang, Yizhe Zhang, Xiujun Li, Michel Galley, Chris Brockett, Mengdi Wang, and Jianfeng Gao. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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A systematic characterization of sampling algorithms for open-ended language generation
Moin Nadeem, Tianxing He, Kyunghyun Cho, and James Glass. 2020 · 2020
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SimCSE: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Mauve: Measuring the gap between neural text and human text using divergence frontiers
Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, John Thickstun, Sean Welleck, Yejin Choi, and Zaid Harchaoui. 2021 · 2021
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BRIO: Bringing order to abstractive summarization
Yixin Liu, Pengfei Liu, Dragomir Radev, and Graham Neubig. 2022 · 2022
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Typical decoding for natural language generation
Clara Meister, Tiago Pimentel, Gian Wiher, and Ryan Cotterell. 2022 · 2022
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In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al. 2022 · 2022
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A contrastive framework for neural text generation
Yixuan Su, Tian Lan, Yan Wang, Dani Yogatama, Lingpeng Kong, and Nigel Collier. 2022 · 2022
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An empirical study on contrastive search and contrastive decoding for open-ended text generation
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Truncation sampling as language model desmoothing
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A simple contrastive learning objective for alleviating neural text degeneration
Shaojie Jiang, Ruqing Zhang, Svitlana Vakulenko, and Maarten de Rijke. 2022 · 2022
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Contrastive decoding: Open-ended text generation as optimization
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Opt: Open pre-trained transformer language models
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Calibrating sequence likelihood improves conditional language generation
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