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Despite the recent progress in language generation models, their outputs may not always meet user expectations.
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 1901
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On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
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Make the most of prior data: A solution for interactive text summarization with preference feedback
Duy-Hung Nguyen, Nguyen Viet Dung Nghiem, Bao-Sinh Nguyen, Dung Tien Tien Le, Shahab Sabahi, Minh-Tien Nguyen, and Hung Le. 2022 · 1930
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A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams and David Zipser. 1989 · 1989
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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Computing krippendorff’s alpha-reliability
Klaus Krippendorff. 2011 · 2011
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014 · 2014
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies
Max Grusky, Mor Naaman, and Yoav Artzi. 2018 · 2018
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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
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Parallel iterative edit models for local sequence transduction
Abhijeet Awasthi, Sunita Sarawagi, Rasna Goyal, Sabyasachi Ghosh, and Vihari Piratla. 2019 · 2019
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Levenshtein Transformer . Curran Associates Inc., Red Hook, NY, USA
Jiatao Gu, Changhan Wang, and Jake Zhao Junbo. 2019 · 2019
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Encode, tag, realize: High-precision text editing
Eric Malmi, Sebastian Krause, Sascha Rothe, Daniil Mirylenka, and Aliaksei Severyn. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Intrinsic evaluation of summarization datasets
Rishi Bommasani and Claire Cardie. 2020 · 2020
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Factual error correction for abstractive summarization models
Meng Cao, Yue Dong, Jiapeng Wu, and Jackie Chi Kit Cheung. 2020 · 2020
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Evaluating factuality in generation with dependency-level entailment
Tanya Goyal and Greg Durrett. 2020 · 2020
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What have we achieved on text summarization?
Dandan Huang, Leyang Cui, Sen Yang, Guangsheng Bao, Kun Wang, Jun Xie, and Yue Zhang. 2020 · 2020
Earlier work this paper cites.
Evaluating the factual consistency of abstractive text summarization
Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2020 · 2020
Earlier work this paper cites.
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.
FELIX: Flexible text editing through tagging and insertion
Jonathan Mallinson, Aliaksei Severyn, Eric Malmi, and Guillermo Garrido. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Cited alongside, same era.
Seq2Edits: Sequence transduction using span-level edit operations
Felix Stahlberg and Shankar Kumar. 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.
An imitation learning curriculum for text editing with non-autoregressive models
Sweta Agrawal and Marine Carpuat. 2022 · 2022
Closest in time.
PromptSource: An integrated development environment and repository for natural language prompts
Stephen Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-david, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Fries, Maged Al-shaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Dragomir Radev, Mike Tian-jian Jiang, and Alexander Rush. 2022 · 2022
Closest in time.
Correcting diverse factual errors in abstractive summarization via post-editing and language model infilling
Vidhisha Balachandran, Hannaneh Hajishirzi, William W. Cohen, and Yulia Tsvetkov. 2022 · 2022
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Hallucinated but factual! inspecting the factuality of hallucinations in abstractive summarization
Meng Cao, Yue Dong, and Jackie Cheung. 2022 · 2022
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QAFactEval: Improved QA-based factual consistency evaluation for summarization
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Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2020
Cited alongside, same era.
Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2020 · 2020
Cited alongside, same era.
Focus attention: Promoting faithfulness and diversity in summarization
Rahul Aralikatte, Shashi Narayan, Joshua Maynez, Sascha Rothe, and Ryan McDonald. 2021 · 2021
Cited alongside, same era.
CLIFF: Contrastive learning for improving faithfulness and factuality in abstractive summarization
Shuyang Cao and Lu Wang. 2021 · 2021
Cited alongside, same era.
Improving faithfulness in abstractive summarization with contrast candidate generation and selection
Sihao Chen, Fan Zhang, Kazoo Sone, and Dan Roth. 2021 · 2021
Cited alongside, same era.
SummEval: Re-evaluating summarization evaluation
Alexander R. Fabbri, Wojciech Kryściński, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2021 · 2021
Cited alongside, same era.
Text editing by command
Felix Faltings, Michel Galley, Gerold Hintz, Chris Brockett, Chris Quirk, Jianfeng Gao, and Bill Dolan. 2021 · 2021
Cited alongside, same era.
Alexander Fabbri, Chien-Sheng Wu, Wenhao Liu, and Caiming Xiong. 2022a · 2022
Closest in time.
Fan Huang, Haewoon Kwak, and Jisun An. 2022 · 2022
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Maieutic prompting: Logically consistent reasoning with recursive explanations
Jaehun Jung, Lianhui Qin, Sean Welleck, Faeze Brahman, Chandra Bhagavatula, Ronan Le Bras, and Yejin Choi. 2022 · 2022
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Edit5: Semi-autoregressive text-editing with t5 warm-start
Jonathan Mallinson, Jakub Adamek, Eric Malmi, and Aliaksei Severyn. 2022 · 2022
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Text generation with text-editing models
Eric Malmi, Yue Dong, Jonathan Mallinson, Aleksandr Chuklin, Jakub Adamek, Daniil Mirylenka, Felix Stahlberg, Sebastian Krause, Shankar Kumar, and Aliaksei Severyn. 2022 · 2022
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M Rush. 2022 · 2022
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Training language models with language feedback
Jérémy Scheurer, Jon Ander Campos, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez. 2022 · 2022
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Peer: A collaborative language model
Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, and Sebastian Riedel. 2022 · 2022
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Understanding factual errors in summarization: Errors, summarizers, datasets, error detectors
Liyan Tang, Tanya Goyal, Alexander R. Fabbri, Philippe Laban, Jiacheng Xu, Semih Yahvuz, Wojciech Kryscinski, Justin F. Rousseau, and Greg Durrett. 2022 · 2022
Closest in time.
FactPEGASUS: Factuality-aware pre-training and fine-tuning for abstractive summarization
David Wan and Mohit Bansal. 2022 · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
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DataLab: A platform for data analysis and intervention
Yang Xiao, Jinlan Fu, Weizhe Yuan, Vijay Viswanathan, Zhoumianze Liu, Yixin Liu, Graham Neubig, and Pengfei Liu. 2022 · 2022
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Learning new skills after deployment: Improving open-domain internet-driven dialogue with human feedback
Jing Xu, Megan Ung, Mojtaba Komeili, Kushal Arora, Y-Lan Boureau, and Jason Weston. 2022 · 2022
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Jointly learning guidance induction and faithful summary generation via conditional variational autoencoders
Wang Xu and Tiejun Zhao. 2022 · 2022
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Improving the faithfulness of abstractive summarization via entity coverage control
Haopeng Zhang, Semih Yavuz, Wojciech Kryscinski, Kazuma Hashimoto, and Yingbo Zhou. 2022 · 2022
Closest in time.