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Large Language Models have emerged as prime candidates to tackle misinformation mitigation.
On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017 · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal. 2017 · 2017
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Fake news detection on social media: A data mining perspective
Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, and Huan Liu. 2017 · 2017
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" liar, liar pants on fire": A new benchmark dataset for fake news detection
William Yang Wang. 2017 · 2017
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Combating fake news: A survey on identification and mitigation techniques
Karishma Sharma, Feng Qian, He Jiang, Natali Ruchansky, Ming Zhang, and Yan Liu. 2019 · 2019
Earlier work this paper cites.
Fake news, rumor, information pollution in social media and web: A contemporary survey of state-of-the-arts, challenges and opportunities
Priyanka Meel and Dinesh Kumar Vishwakarma. 2020 · 2020
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Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods
Eyke Hüllermeier and Willem Waegeman. 2021 · 2021
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Fakebert: Fake news detection in social media with a bert-based deep learning approach
Rohit Kumar Kaliyar, Anurag Goswami, and Pratik Narang. 2021 · 2021
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Measuring the impact of covid-19 vaccine misinformation on vaccination intent in the uk and usa
Sahil Loomba, Alexandre de Figueiredo, Simon J Piatek, Kristen de Graaf, and Heidi J Larson. 2021 · 2021
Cited alongside, same era.
The surprising performance of simple baselines for misinformation detection
Kellin Pelrine, Jacob Danovitch, and Reihaneh Rabbany. 2021 · 2021
Cited alongside, same era.
An adversarial benchmark for fake news detection models
Lorenzo Jaime Yu Flores and Yiding Hao. 2022 · 2022
Cited alongside, same era.
Teaching models to express their uncertainty in words
Stephanie Lin, Jacob Hilton, and Owain Evans. 2022 · 2022
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023 · 2023
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Generating with confidence: Uncertainty quantification for black-box large language models
Zhen Lin, Shubhendu Trivedi, and Jimeng Sun. 2023 · 2023
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Selfcheckgpt: Zero-resource black-box hallucination detection for generative large language models
Potsawee Manakul, Adian Liusie, and Mark JF Gales. 2023 · 2023
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Fact focus: Fake image of pentagon explosion briefly sends jitters through stock market
Philip Marcelo. 2023 · 2023
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Towards reliable misinformation mitigation: Generalization, uncertainty, and gpt-4
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
Cited alongside, same era.
Jiuhai Chen and Jonas Mueller. 2023 · 2023
Cited alongside, same era.
Look before you leap: An exploratory study of uncertainty measurement for large language models
Yuheng Huang, Jiayang Song, Zhijie Wang, Huaming Chen, and Lei Ma. 2023 · 2023
Cited alongside, same era.
Kellin Pelrine, Meilina Reksoprodjo, Caleb Gupta, Joel Christoph, and Reihaneh Rabbany. 2023 · 2023
Later among the works it cites.
The perils & promises of fact-checking with large language models
Dorian Quelle and Alexandre Bovet. 2023 · 2023
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Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher D Manning. 2023 · 2023
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Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms
Miao Xiong, Zhiyuan Hu, Xinyang Lu, Yifei Li, Jie Fu, Junxian He, and Bryan Hooi. 2023 · 2023
Later among the works it cites.