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Despite recent success, large neural models often generate factually incorrect text.
Towards content transfer through grounded text generation
Shrimai Prabhumoye, Chris Quirk, and Michel Galley. 2019 · 1905
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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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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Automatic evaluation of machine translation quality using n-gram co-occurrence statistics
George Doddington. 2002 · 2002
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Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton. 2002 · 2002
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Asking and answering questions to evaluate the factual consistency of summaries
Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020 · 2004
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Language models are few-shot learners
T. Brown, B. Mann, Nick Ryder, Melanie Subbiah, J. Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, G. Krüger, T. Henighan, R. Child, Aditya Ramesh, D. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, E. Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, J. Clark, Christopher Berner, Sam McCandlish, A. Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2005
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Computing krippendorff’s alpha reliability
Klaus Krippendorff. 2007 · 2007
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Go figure! a meta evaluation of factuality in summarization
Saadia Gabriel, A. Çelikyilmaz, R. Jha, Yejin Choi, and Jianfeng Gao. 2020 · 2010
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Constrained abstractive summarization: Preserving factual consistency with constrained generation
Yuning Mao, Xiang Ren, Heng Ji, and Jiawei Han. 2020 · 2010
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Meteor universal: Language specific translation evaluation for any target language
Michael Denkowski and Alon Lavie. 2014 · 2014
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A neural attention model for abstractive sentence summarization
Alexander M Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
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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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Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Ranking generated summaries by correctness: An interesting but challenging application for natural language inference
Tobias Falke, Leonardo F. R. Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych. 2019 · 2019
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Assessing the factual accuracy of generated text
Ben Goodrich, Vinay Rao, Peter J Liu, and Mohammad Saleh. 2019 · 2019
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 2019
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Focus attention: Promoting faithfulness and diversity in summarization
Rahul Aralikatte, Shashi Narayan, Joshua Maynez, Sascha Rothe, and Ryan McDonald. 2021 · 2021
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On the dangers of stochastic parrots: Can language models be too big?
Emily Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
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CLIFF: Contrastive learning for improving faithfulness and factuality in abstractive summarization
Shuyang Cao and Lu Wang. 2021 · 2021
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Evaluating groundedness in dialogue systems: The begin benchmark
Nouha Dziri, Hannah Rashkin, Tal Linzen, and D. Reitter. 2021 · 2021
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Annotating and modeling fine-grained factuality in summarization
Tanya Goyal and Greg Durrett. 2021 · 2021
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Conversing by reading: Contentful neural conversation with on-demand machine reading
Lianhui Qin, Michel Galley, Chris Brockett, Xiaodong Liu, Xiang Gao, Bill Dolan, Yejin Choi, and Jianfeng Gao. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
Esin Durmus, He He, and Mona Diab. 2020 · 2020
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Improved natural language generation via loss truncation
Daniel Kang and Tatsunori Hashimoto. 2020 · 2020
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Evaluating the factual consistency of abstractive text summarization
Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2020 · 2020
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Beyond accuracy: Behavioral testing of nlp models with checklist
Marco Túlio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020 · 2020
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Optimizing the factual correctness of a summary: A study of summarizing radiology reports
Yuhao Zhang, Derek Merck, Emily Tsai, Christopher D Manning, and Curtis Langlotz. 2020 · 2020
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Tianyu Liu, Xin Zheng, Baobao Chang, and Zhifang Sui. 2021 · 2021
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Improving factual consistency of abstractive summarization via question answering
Feng Nan, Cícero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Kathleen McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, and Bing Xiang. 2021 · 2021
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Understanding factuality in abstractive summarization with FRANK: A benchmark for factuality metrics
Artidoro Pagnoni, Vidhisha Balachandran, and Yulia Tsvetkov. 2021 · 2021
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Retrieval augmentation reduces hallucination in conversation
Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston. 2021 · 2021
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Factual consistency evaluation for text summarization via counterfactual estimation
Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, and Bolin Ding. 2021 · 2021
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Dissecting generation modes for abstractive summarization models via ablation and attribution
Jiacheng Xu and Greg Durrett. 2021 · 2021
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Enhancing factual consistency of abstractive summarization
Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, and Meng Jiang. 2021 · 2021
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