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The advent of large language models (LLMs) has significantly advanced natural language processing tasks like text summarization.
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
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Distilling task-specific knowledge from bert into simple neural networks
Raphael Tang, Yao Lu, Linqing Liu, Lili Mou, Olga Vechtomova, and Jimmy Lin. 2019 · 1903
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 1908
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Tinybert: Distilling bert for natural language understanding
Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, and Qun Liu. 2019 · 1909
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 1910
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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Distilling knowledge learned in bert for text generation
Yen-Chun Chen, Zhe Gan, Yu Cheng, Jingzhou Liu, and Jingjing Liu. 2019 · 1911
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Introduction to the special issue on summarization
Dragomir R. Radev, Eduard Hovy, and Kathleen McKeown. 2002 · 2002
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Reclor: A reading comprehension dataset requiring logical reasoning
Weihao Yu, Zihang Jiang, Yanfei Dong, and Jiashi Feng. 2020 · 2002
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Latent dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan. 2003 · 2003
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Large language models in machine translation
Thorsten Brants, Ashok C Popat, Peng Xu, Franz J Och, and Jeffrey Dean. 2007 · 2007
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Modeling annotators: A generative approach to learning from annotator rationales
Omar Zaidan and Jason Eisner. 2008 · 2008
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
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Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. 2015 · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
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Abstractive text summarization using sequence-to-sequence rnns and beyond
Ramesh Nallapati, Bowen Zhou, Caglar Gulcehre, Bing Xiang, et al. 2016 · 2016
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. 2016 · 2016
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim. 2017 · 2017
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On the power of curriculum learning in training deep networks
Guy Hacohen and Daphna Weinshall. 2019 · 2019
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Text summarization with pretrained encoders
Yang Liu. 2019 · 2019
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Energy and policy considerations for deep learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 2019
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End-to-end open-domain question answering with BERTserini
Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019 · 2019
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Knowledge distillation on extractive summarization
Ying-Jia Lin, Daniel Tan, Tzu-Hsuan Chou, Hung-Yu Kao, and Hsin-Yang Wang. 2020 · 2020
GLM: General language model pretraining with autoregressive blank infilling
Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang. 2022 · 2022
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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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Pinto: Faithful language reasoning using prompt-generated rationales
Peifeng Wang, Aaron Chan, Filip Ilievski, Muhao Chen, and Xiang Ren. 2022 · 2022
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Sequence level contrastive learning for text summarization
Shusheng Xu, Xingxing Zhang, Yi Wu, and Furu Wei. 2022 · 2022
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Attention temperature matters in abstractive summarization distillation
Shengqiang Zhang, Xingxing Zhang, Hangbo Bao, and Furu Wei. 2022 · 2022
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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.
Pre-trained summarization distillation
Sam Shleifer and Alexander M. Rush. 2020 · 2020
Cited alongside, same era.
Curriculum learning for natural language understanding
Benfeng Xu, Licheng Zhang, Zhendong Mao, Quan Wang, Hongtao Xie, and Yongdong Zhang. 2020 · 2020
Cited alongside, same era.
Pegasus: Pre-training with extracted gap-sentences for abstractive summarization
Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter Liu. 2020 · 2020
Cited alongside, same era.
PLATO-2: Towards building an open-domain chatbot via curriculum learning
Siqi Bao, Huang He, Fan Wang, Hua Wu, Haifeng Wang, Wenquan Wu, Zhen Guo, Zhibin Liu, and Xinchao Xu. 2021 · 2021
Cited alongside, same era.
GSum: A general framework for guided neural abstractive summarization
Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi, Zhengbao Jiang, and Graham Neubig. 2021 · 2021
Cited alongside, same era.
Trueteacher: Learning factual consistency evaluation with large language models
Zorik Gekhman, Jonathan Herzig, Roee Aharoni, Chen Elkind, and Idan Szpektor. 2023 · 2023
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Large language models are reasoning teachers
Namgyu Ho, Laura Schmid, and Se-Young Yun. 2023 · 2023
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Cheng-Yu Hsieh, Chun-Liang Li, Chih-Kuan Yeh, Hootan Nakhost, Yasuhisa Fujii, Alexander Ratner, Ranjay Krishna, Chen-Yu Lee, and Tomas Pfister. 2023 · 2023
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On learning to summarize with large language models as references
Yixin Liu, Alexander R Fabbri, Pengfei Liu, Dragomir Radev, and Arman Cohan. 2023 · 2023
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Teaching small language models to reason
Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, Eric Malmi, and Aliaksei Severyn. 2023 · 2023
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Factscore: Fine-grained atomic evaluation of factual precision in long form text generation
Sewon Min, Kalpesh Krishna, Xinxi Lyu, Mike Lewis, Wen tau Yih, Pang Wei Koh, Mohit Iyyer, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023 · 2023
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OpenAI. 2023 · 2023
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Distilling reasoning capabilities into smaller language models
Kumar Shridhar, Alessandro Stolfo, and Mrinmaya Sachan. 2023 · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. 2023 · 2023
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AnyPredict: Foundation model for tabular prediction
Zifeng Wang, Chufan Gao, Cao Xiao, and Jimeng Sun. 2023 · 2023
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou. 2023 · 2023
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Exploring the limits of chatgpt for query or aspect-based text summarization
Xianjun Yang, Yan Li, Xinlu Zhang, Haifeng Chen, and Wei Cheng. 2023 · 2023
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