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The present study introduces the knowledge-augmented generator, which is specifically designed to produce information that remains grounded in contextual knowledge, regardless of alterations in the context.
On faithfulness and factuality in abstractive summarization
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Diversity driven attention model for query-based abstractive summarization
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Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick S. H. Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020b · 2020
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Ambigqa: Answering ambiguous open-domain questions
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Domain robustness in neural machine translation
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020 · 2020
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$qˆ2$: Evaluating factual consistency in knowledge-grounded dialogues via question generation and question answering
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On the origin of hallucinations in conversational models: Is it the datasets or the models?
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Few-shot learning with retrieval augmented language models
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Survey of hallucination in natural language generation
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Realtime QA: what’s the answer right now?
Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, Ronan Le Bras, Akari Asai, Xinyan Yu, Dragomir R. Radev, Noah A. Smith, Yejin Choi, and Kentaro Inui. 2022 · 2022
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Leveraging passage retrieval with generative models for open domain question answering
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Hurdles to progress in long-form question answering
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Entity-based knowledge conflicts in question answering
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New methods & metrics for LFQA tasks
Suchismit Mahapatra, Vladimir Blagojevic, Pablo Bertorello, and Prasanna Kumar. 2021 · 2021
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Faithful or extractive? on mitigating the faithfulness-abstractiveness trade-off in abstractive summarization
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Streamingqa: A benchmark for adaptation to new knowledge over time in question answering models
Adam Liska, Tomás Kociský, Elena Gribovskaya, Tayfun Terzi, Eren Sezener, Devang Agrawal, Cyprien de Masson d’Autume, Tim Scholtes, Manzil Zaheer, Susannah Young, Ellen Gilsenan-McMahon, Sophia Austin, Phil Blunsom, and Angeliki Lazaridou. 2022 · 2022
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Read before generate! faithful long form question answering with machine reading
Dan Su, Xiaoguang Li, Jindi Zhang, Lifeng Shang, Xin Jiang, Qun Liu, and Pascale Fung. 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
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Archivalqa: A large-scale benchmark dataset for open-domain question answering over historical news collections
Jiexin Wang, Adam Jatowt, and Masatoshi Yoshikawa. 2022 · 2022
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Probing factually grounded content transfer with factual ablation
Peter West, Chris Quirk, Michel Galley, and Yejin Choi. 2022 · 2022
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Temporal knowledge question answering via abstract reasoning induction
Ziyang Chen, Dongfang Li, Xiang Zhao, Baotian Hu, and Min Zhang. 2023 · 2023
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Training dynamics for text summarization models
Tanya Goyal, Jiacheng Xu, Junyi Jessy Li, and Greg Durrett. 2022 · 2073
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