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Recent work has identified noisy and misannotated data as a core cause of hallucinations and unfaithful outputs in Natural Language Generation (NLG) tasks.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
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Okapi at trec-3
Stephen E. Robertson, Steve Walker, Susan Jones, Micheline Hancock-Beaulieu, and Mike Gatford. 1994 · 1994
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Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020 · 2003
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Mind the facts: Knowledge-boosted coherent abstractive text summarization
Beliz Gunel, Chenguang Zhu, Michael Zeng, and Xuedong Huang. 2020 · 2006
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The new york times annotated corpus
Evan Sandhaus. 2008 · 2008
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Explaining and improving model behavior with k nearest neighbor representations
Nazneen Rajani, Ben Krause, Wengpeng Yin, Tong Niu, Richard Socher, and Caiming Xiong. 2020 · 2010
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Unbiased look at dataset bias
Antonio Torralba and Alexei A. Efros. 2011 · 2011
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spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani. 2017 · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang. 2017 · 2017
Earlier work this paper cites.
The E2E dataset: New challenges for end-to-end generation
Jekaterina Novikova, Ondřej Dušek, and Verena Rieser. 2017 · 2017
Earlier work this paper cites.
Faithful to the original: Fact-aware neural abstractive summarization
Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018 · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Representer point selection for explaining deep neural networks
Chih-Kuan Yeh, Joon Kim, Ian En-Hsu Yen, and Pradeep K Ravikumar. 2018 · 2018
Cited alongside, same era.
The secret sharer: Evaluating and testing unintended memorization in neural networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson, Jernej Kos, and Dawn Song. 2019 · 2019
Cited alongside, same era.
Semantic noise matters for neural natural language generation
Ondřej Dušek, David M. Howcroft, and Verena Rieser. 2019 · 2019
Cited alongside, same era.
Data cleansing for models trained with sgd
Explaining black box predictions and unveiling data artifacts through influence functions
Xiaochuang Han, Byron C. Wallace, and Yulia Tsvetkov. 2020 · 2020
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Improved natural language generation via loss truncation
Daniel Kang and Tatsunori B. Hashimoto. 2020 · 2020
Later among the works it 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
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Estimating training data influence by tracing gradient descent
Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan. 2020 · 2020
Later among the works it cites.
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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Satoshi Hara, Atsushi Nitanda, and Takanori Maehara. 2019 · 2019
Cited alongside, same era.
Quantifying Gender Bias in Different Corpora , page 752–759. Association for Computing Machinery, New York, NY, USA
Marzieh Babaeianjelodar, Stephen Lorenz, Josh Gordon, Jeanna Matthews, and Evan Freitag. 2020 · 2020
Cited alongside, same era.
Relatif: Identifying explanatory training examples via relative influence
Elnaz Barshan, Marc-Etienne Brunet, and Gintare Karolina Dziugaite. 2020 · 2020
Cited alongside, same era.
FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization
Esin Durmus, He He, and Mona Diab. 2020 · 2020
Cited alongside, same era.
Evaluating the state-of-the-art of end-to-end natural language generation: The e2e nlg challenge
Ondřej Dušek, Jekaterina Novikova, and Verena Rieser. 2020 · 2020
Cited alongside, same era.
Entity-level factual consistency of abstractive text summarization
Feng Nan, Ramesh Nallapati, Zhiguo Wang, Cicero Nogueira dos Santos, Henghui Zhu, Dejiao Zhang, Kathleen McKeown, and Bing Xiang. 2021a
Cited in the paper.
Improving factual consistency of abstractive summarization via question answering
Feng Nan, Cicero Nogueira dos Santos, Henghui Zhu, Patrick Ng, Kathleen McKeown, Ramesh Nallapati, Dejiao Zhang, Zhiguo Wang, Andrew O. Arnold, and Bing Xiang. 2021b
Cited in the paper.
Peter West, Chandrasekhar Bhagavatula, Jack Hessel, Jena D. Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, and Yejin Choi. 2021 · 2021
Later among the works it cites.
Tracing knowledge in language models back to the training data
Ekin Akyürek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, and Kelvin Guu. 2022 · 2022
Closest in time.
Datamodels: Predicting predictions from training data
Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, and Aleksander Madry. 2022 · 2022
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Scaling up influence functions
Andrea Schioppa, Polina Zablotskaia, David Vilar Torres, and Artem Sokolov. 2022 · 2022
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Simfluence: Modeling the influence of individual training examples by simulating training runs
Kelvin Guu, Albert Webson, Elizabeth-Jane Pavlick, Lucas Dixon, Ian Tenney, and Tolga Bolukbasi. 2023 · 2023
Closest in time.