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Large language models (LLMs) have become mainstream technology with their versatile use cases and impressive performance.
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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Evaluating the factual consistency of abstractive text summarization
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A richly annotated corpus for different tasks in automated fact-checking
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Belief in conspiracy theories
Ted Goertzel. 1994 · 1994
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Conspiracy theories
Cass R Sunstein and Adrian Vermeule. 2008 · 2008
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Kilt: a benchmark for knowledge intensive language tasks
Fabio Petroni, Aleksandra Piktus, Angela Fan, Patrick Lewis, Majid Yazdani, Nicola De Cao, James Thorne, Yacine Jernite, Vladimir Karpukhin, Jean Maillard, et al. 2020 · 2009
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Unanswered questions: A preliminary investigation of personality and individual difference predictors of 9/11 conspiracist beliefs
Viren Swami, Tomas Chamorro-Premuzic, and Adrian Furnham. 2010 · 2010
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Measuring belief in conspiracy theories: The generic conspiracist beliefs scale
Robert Brotherton, Christopher C French, and Alan D Pickering. 2013 · 2013
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Commercial conspiracy theories: A pilot study
Adrian Furnham. 2013 · 2013
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The measurement and prediction of conspiracy beliefs
Chelsea Rose. 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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Where is your evidence: Improving fact-checking by justification modeling
Tariq Alhindi, Savvas Petridis, and Smaranda Muresan. 2018 · 2018
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Universal sentence encoder for English
Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St. John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, Brian Strope, and Ray Kurzweil. 2018 · 2018
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Negligent falsehood, white ignorance, and false news
Jennifer Saul, E Michaelson, and A Stokke. 2018 · 2018
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Belief in conspiracy theories: Basic principles of an emerging research domain
Jan-Willem van Prooijen and Karen M Douglas. 2018 · 2018
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Connecting the dots: Illusory pattern perception predicts belief in conspiracies and the supernatural
Jan-Willem Van Prooijen, Karen M Douglas, and Clara De Inocencio. 2018 · 2018
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Increased conspiracy beliefs among ethnic and muslim minorities
Jan-Willem van Prooijen, Jaap Staman, and André PM Krouwel. 2018 · 2018
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Assessing the factual accuracy of generated text
Ben Goodrich, Vinay Rao, Peter J Liu, and Mohammad Saleh. 2019 · 2019
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The industrialization of terrorist propaganda - middlebury.edu
Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Finding someone to blame: The link between covid-19 conspiracy beliefs, prejudice, support for violence, and other negative social outcomes
Jakub Šrol, Vladimíra Čavojová, and Eva Ballová Mikušková. 2022 · 2022
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Evaluating the factual consistency of large language models through summarization
Derek Tam, Anisha Mascarenhas, Shiyue Zhang, Sarah Kwan, Mohit Bansal, and Colin Raffel. 2022 · 2022
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Emergent abilities of large language models
Barret Zoph, Colin Raffel, Dale Schuurmans, Dani Yogatama, Denny Zhou, Don Metzler, Ed H. Chi, Jason Wei, Jeff Dean, Liam B. Fedus, Maarten Paul Bosma, Oriol Vinyals, Percy Liang, Sebastian Borgeaud, Tatsunori B. Hashimoto, and Yi Tay. 2022 · 2022
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Devising tests to measure gpt-3’s knowledge of the basic sciences
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Alex Newhouse, Jason Blazakis, and Kris McGuffie. 2019 · 2019
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Checkthat! at clef 2020: Enabling the automatic identification and verification of claims in social media
Alberto Barrón-Cedeno, Tamer Elsayed, Preslav Nakov, Giovanni Da San Martino, Maram Hasanain, Reem Suwaileh, and Fatima Haouari. 2020 · 2020
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The pile: An 800gb dataset of diverse text for language modeling
Leo Gao, Stella Rose Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. 2020 · 2020
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Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano. 2020 · 2020
Cited alongside, same era.
Measuring and improving consistency in pretrained language models
Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, and Yoav Goldberg. 2021 · 2021
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Truthfulqa: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans. 2021 · 2021
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Is GPT-3 text indistinguishable from human text? scarecrow: A framework for scrutinizing machine text
Yao Dou, Maxwell Forbes, Rik Koncel-Kedziorski, Noah A. Smith, and Yejin Choi. 2022 · 2022
Cited alongside, same era.
Luciano Abriata. 2021 · 2023
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Exploring token probabilities as a means to filter gpt-3’s answers
Luciano Abriata. 2023 · 2023
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50 fox news ’lies’ in 6 seconds, from ’the daily show’
Lauren Carroll and Aaron Sharockman. 2015 · 2023
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Prompting gpt-3 to be reliable
Silei Cheng, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Boyd-Graber, and Lijuan Wang. 2023 · 2023
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AI trained on 4chan becomes ‘hate speech machine’
Matthew Gault. 2022 · 2023
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Disinformation researchers raise alarms about A.I. chatbots
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Fine-tuning a classifier to improve truthfulness
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OpenAI API examples
OpenAI. 2023 · 2023
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