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Fact verification aims to verify a claim using evidence from a trustworthy knowledge base.
Relational graph attention networks
Dan Busbridge, Dane Sherburn, Pietro Cavallo, and Nils Y Hammerla · 1904
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Multifc: A real-world multi-domain dataset for evidence-based fact checking of claims
Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, and Jakob Grue Simonsen · 1909
Earlier work this paper cites.
Knowledge-enhanced graph attention network for fact verification
Chonghao Chen, Jianming Zheng, and Honghui Chen · 1949
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Big self-supervised models are strong semi-supervised learners
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey Hinton · 2006
Earlier work this paper cites.
Ranking measures and loss functions in learning to rank
Wei Chen, Tie-yan Liu, Yanyan Lan, Zhi-ming Ma, and Hang Li · 2009
Earlier work this paper cites.
Distilling the knowledge in a neural network, 2015
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
Earlier work this paper cites.
Fitnets: Hints for thin deep nets, 2015
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2015
Earlier work this paper cites.
Representing text for joint embedding of text and knowledge bases
Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon · 2015
Earlier work this paper cites.
Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer, 2017
Sergey Zagoruyko and Nikos Komodakis · 2017
Earlier work this paper cites.
Qed: A fact verification system for the fever shared task
Jackson Luken, Nan-Jiang Jiang, and Marie-Catherine de Marneffe · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
Earlier work this paper cites.
FEVER: a large-scale dataset for fact extraction and verification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal · 2018
Earlier work this paper cites.
Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
Earlier work this paper cites.
Twowingos: A two-wing optimization strategy for evidential claim verification, 2018
Wenpeng Yin and Dan Roth · 2018
Earlier work this paper cites.
Detection and resolution of rumours in social media: A survey
Arkaitz Zubiaga, Ahmet Aker, Kalina Bontcheva, Maria Liakata, and Rob Procter · 2018
Earlier work this paper cites.
Bidirectional attentive memory networks for question answering over knowledge bases
Yu Chen, Lingfei Wu, and Mohammed J Zaki · 2019
Earlier work this paper cites.
Transformer-xl: Attentive language models beyond a fixed-length context, 2019
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, and Ruslan Salakhutdinov · 2019
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Earlier work this paper cites.
Analyzing and improving representations with the soft nearest neighbor loss, 2019
Nicholas Frosst, Nicolas Papernot, and Geoffrey Hinton · 2019
Earlier work this paper cites.
Unsupervised question answering for fact-checking
Mayank Jobanputra · 2019
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Roberta: A robustly optimized bert pretraining approach, 2019
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Earlier work this paper cites.
Relational knowledge distillation, 2019
Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Kg-bert: Bert for knowledge graph completion, 2019
Liang Yao, Chengsheng Mao, and Yuan Luo · 2019
Cited alongside, same era.
Gear: Graph-based evidence aggregating and reasoning for fact verification, 2019
Jie Zhou, Xu Han, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2019
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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
Cited alongside, same era.
Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Structure-augmented text representation learning for efficient knowledge graph completion
Bo Wang, Tao Shen, Guodong Long, Tianyi Zhou, Ying Wang, and Yi Chang · 2021
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Knowledge enhanced fact checking and verification
Biru Zhu, Xingyao Zhang, Ming Gu, and Yangdong Deng · 2021
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Knowledge is flat: A Seq2Seq generative framework for various knowledge graph completion
Chen Chen, Yufei Wang, Bing Li, and Kwok-Yan Lam · 2022
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The state of human-centered NLP technology for fact-checking
Anubrata Das, Houjiang Liu, Venelin Kovatchev, and Matthew Lease · 2022
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Paragraph-based transformer pre-training for multi-sentence inference
Luca Di Liello, Siddhant Garg, Luca Soldaini, and Alessandro Moschitti · 2022
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A Survey on Automated Fact-Checking
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An entity-graph based reasoning method for fact verification
Chonghao Chen, Fei Cai, Xuejun Hu, Jianming Zheng, Yanxiang Ling, and Honghui Chen · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning, 2020
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
Unsupervised fact checking by counter-weighted positive and negative evidential paths in a knowledge graph
Jiseong Kim and Key-sun Choi · 2020
Cited alongside, same era.
Language models as fact checkers?
Nayeon Lee, Belinda Z. Li, Sinong Wang, Wen-tau Yih, Hao Ma, and Madian Khabsa · 2020
Cited alongside, same era.
Improving multi-hop question answering over knowledge graphs using knowledge base embeddings
Apoorv Saxena, Aditay Tripathi, and Partha Talukdar · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Knowledge distillation meets self-supervision, 2020
Guodong Xu, Ziwei Liu, Xiaoxiao Li, and Chen Change Loy · 2020
Cited alongside, same era.
Zhijiang Guo, Michael Schlichtkrull, and Andreas Vlachos · 2022
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Kelm: Knowledge enhanced pre-trained language representations with message passing on hierarchical relational graphs, 2022
Yinquan Lu, Haonan Lu, Guirong Fu, and Qun Liu · 2022
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Lemon: Language model for negative sampling of knowledge graph embeddings
Md. Rashad Al Hasan Rony, Mirza Mohtashim Alam, Semab Ali, Jens Lehmann, and Sahar Vahdati · 2022
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Contrastive representation distillation, 2022
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2022
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Simkgc: Simple contrastive knowledge graph completion with pre-trained language models, 2022
Liang Wang, Wei Zhao, Zhuoyu Wei, and Jingming Liu · 2022
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Progcl: Rethinking hard negative mining in graph contrastive learning
Jun Xia, Lirong Wu, Ge Wang, and Stan Z. Li · 2022
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LaPraDoR: Unsupervised pretrained dense retriever for zero-shot text retrieval
Canwen Xu, Daya Guo, Nan Duan, and Julian McAuley · 2022
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Knowledge graph contrastive learning for recommendation
Yuhao Yang, Chao Huang, Lianghao Xia, and Chenliang Li · 2022
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React: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao · 2022
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A survey of safety and trustworthiness of large language models through the lens of verification and validation, 2023
Xiaowei Huang, Wenjie Ruan, Wei Huang, Gaojie Jin, Yi Dong, Changshun Wu, Saddek Bensalem, Ronghui Mu, Yi Qi, Xingyu Zhao, Kaiwen Cai, Yanghao Zhang, Sihao Wu, Peipei Xu, Dengyu Wu, Andre Freitas, and Mustafa A. Mustafa · 2023
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Trustworthy llms: a survey and guideline for evaluating large language models’ alignment, 2023
Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo, Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq, and Hang Li · 2023
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Self-supervised distilled learning for multi-modal misinformation identification
Michael Mu, Sreyasee Das Bhattacharjee, and Junsong Yuan · 2023
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Scientific fact-checking: A survey of resources and approaches
Juraj Vladika and Florian Matthes · 2023
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Generate rather than retrieve: Large language models are strong context generators
Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, and Meng Jiang · 2023
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Prompt to be consistent is better than self-consistent? few-shot and zero-shot fact verification with pre-trained language models
Fengzhu Zeng and Wei Gao · 2023
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Generating fluent fact checking explanations with unsupervised post-editing
Shailza Jolly, Pepa Atanasova, and Isabelle Augenstein · 2078
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