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The widespread adoption and transformative effects of large language models (LLMs) have sparked concerns regarding their capacity to produce inaccurate and fictitious content, referred to as `hallucinations'.
Information: Does it have to be true?
James H Fetzer · 2004
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Statistical power analyses using g* power 3.1: Tests for correlation and regression analyses
Franz Faul, Edgar Erdfelder, Axel Buchner, and Albert-Georg Lang · 2009
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Explicit warnings reduce but do not eliminate the continued influence of misinformation
Ullrich KH Ecker, Stephan Lewandowsky, and David TW Tang · 2010
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Mining the correlation between human and automatic evaluation at sentence level
Yanli Sun · 2010
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Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and anovas
Daniël Lakens · 2013
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Misinformation, disinformation, and violent conflict: From iraq and the “war on terror” to future threats to peace
Stephan Lewandowsky, Werner GK Stritzke, Alexandra M Freund, Klaus Oberauer, and Joachim I Krueger · 2013
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Facebook sentiment: Reactions and emojis
Ye Tian, Thiago Galery, Giulio Dulcinati, Emilia Molimpakis, and Chao Sun · 2017
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Networked narratives on humans of new york: A content analysis of social media engagement on facebook
Ruoxu Wang, Jinyoung Kim, Anli Xiao, and Yong Ju Jung · 2017
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Falling for fake news: investigating the consumption of news via social media
Martin Flintham, Christian Karner, Khaled Bachour, Helen Creswick, Neha Gupta, and Stuart Moran · 2018
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Let’s hate together: How people share news in messaging, social, and public networks
Danielle Lottridge and Frank R Bentley · 2018
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Prior exposure increases perceived accuracy of fake news
Gordon Pennycook, Tyrone D Cannon, and David G Rand · 2018
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I do not believe you: How providing a source corrects health misperceptions across social media platforms
Emily K Vraga and Leticia Bode · 2018
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How to unring the bell: A meta-analytic approach to correction of misinformation
Nathan Walter and Sheila T Murphy · 2018
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Handling divergent reference texts when evaluating table-to-text generation
Bhuwan Dhingra, Manaal Faruqui, Ankur Parikh, Ming-Wei Chang, Dipanjan Das, and William Cohen · 2019
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Less than you think: Prevalence and predictors of fake news dissemination on facebook
Andrew Guess, Jonathan Nagler, and Joshua Tucker · 2019
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Trust it or not: Effects of machine-learning warnings in helping individuals mitigate misinformation
Haeseung Seo, Aiping Xiong, and Dongwon Lee · 2019
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Defending against neural fake news
Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi · 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
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Enabling language models to fill in the blanks
Chris Donahue, Mina Lee, and Percy Liang · 2020
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Fake news on facebook and twitter: Investigating how people (don’t) investigate
Christine Geeng, Savanna Yee, and Franziska Roesner · 2020
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Automatic detection of generated text is easiest when humans are fooled
Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, and Douglas Eck · 2020
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Fighting covid-19 misinformation on social media: Experimental evidence for a scalable accuracy-nudge intervention
Gordon Pennycook, Jonathon McPhetres, Yunhao Zhang, Jackson G Lu, and David G Rand · 2020
Cited alongside, same era.
Towards faithful neural table-to-text generation with content-matching constraints
Zhenyi Wang, Xiaoyang Wang, Bang An, Dong Yu, and Changyou Chen · 2020
Cited alongside, same era.
All that’s ‘human’ is not gold: Evaluating human evaluation of generated text
Elizabeth Clark, Tal August, Sofia Serrano, Nikita Haduong, Suchin Gururangan, and Noah A. Smith · 2021
Cited alongside, same era.
Shifting attention to accuracy can reduce misinformation online
Gordon Pennycook, Ziv Epstein, Mohsen Mosleh, Antonio A Arechar, Dean Eckles, and David G Rand · 2021
Cited alongside, same era.
Sashank Santhanam, Behnam Hedayatnia, Spandana Gella, Aishwarya Padmakumar, Seokhwan Kim, Yang Liu, and Dilek Hakkani-Tur · 2021
Fighting fire with fire: The dual role of LLMs in crafting and detecting elusive disinformation
Jason Lucas, Adaku Uchendu, Michiharu Yamashita, Jooyoung Lee, Shaurya Rohatgi, and Dongwon Lee · 2023
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Misinformation warning labels are widely effective: A review of warning effects and their moderating features
Cameron Martel and David G Rand · 2023
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Detecting and mitigating hallucinations in multilingual summarisation
Yifu Qiu, Yftah Ziser, Anna Korhonen, Edoardo Ponti, and Shay Cohen · 2023
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The troubling emergence of hallucination in large language models - an extensive definition, quantification, and prescriptive remediations
Vipula Rawte, Swagata Chakraborty, Agnibh Pathak, Anubhav Sarkar, S.M Towhidul Islam Tonmoy, Aman Chadha, Amit Sheth, and Amitava Das · 2023
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Cataloging prompt patterns to enhance the discipline of prompt engineering
Douglas C Schmidt, Jesse Spencer-Smith, Quchen Fu, and Jules White · 2023
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Cited alongside, same era.
QuestEval: Summarization asks for fact-based evaluation
Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, Benjamin Piwowarski, Jacopo Staiano, Alex Wang, and Patrick Gallinari · 2021
Cited alongside, same era.
Retrieval augmentation reduces hallucination in conversation
Kurt Shuster, Spencer Poff, Moya Chen, Douwe Kiela, and Jason Weston · 2021
Cited alongside, same era.
TURINGBENCH: A benchmark environment for Turing test in the age of neural text generation
Adaku Uchendu, Zeyu Ma, Thai Le, Rui Zhang, and Dongwon Lee · 2021
Cited alongside, same era.
Diving deep into modes of fact hallucinations in dialogue systems
Souvik Das, Sougata Saha, and Rohini Srihari · 2022
Cited alongside, same era.
All the news that’s fit to fabricate: Ai-generated text as a tool of media misinformation
Sarah Kreps, R. Miles McCain, and Miles Brundage · 2022
Cited alongside, same era.
TruthfulQA: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
Cited alongside, same era.
A token-level reference-free hallucination detection benchmark for free-form text generation
Tianyu Liu, Yizhe Zhang, Chris Brockett, Yi Mao, Zhifang Sui, Weizhu Chen, and Bill Dolan · 2022
Cited alongside, same era.
Later among the works it cites.
“why is this misleading?”: Detecting news headline hallucinations with explanations
Jiaming Shen, Jialu Liu, Dan Finnie, Negar Rahmati, Mike Bendersky, and Marc Najork · 2023
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Ai model gpt-3 (dis)informs us better than humans
Giovanni Spitale, Nikola Biller-Andorno, and Federico Germani · 2023
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Does human collaboration enhance the accuracy of identifying llm-generated deepfake texts?
Adaku Uchendu, Jooyoung Lee, Hua Shen, Thai Le, Dongwon Lee, et al · 2023
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Can fighting misinformation have a negative spillover effect? how warnings for the threat of misinformation can decrease general news credibility
Toni GLA van der Meer, Michael Hameleers, and Jakob Ohme · 2023
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AlignScore: Evaluating factual consistency with a unified alignment function
Yuheng Zha, Yichi Yang, Ruichen Li, and Zhiting Hu · 2023
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Siren’s song in the ai ocean: a survey on hallucination in large language models
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
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Lawyer used chatgpt in court—and cited fake cases. a judge is considering sanctions
Forbes · 2024
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A survey on large language model hallucination via a creativity perspective
Xuhui Jiang, Yuxing Tian, Fengrui Hua, Chengjin Xu, Yuanzhuo Wang, and Jian Guo · 2024
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The dawn after the dark: An empirical study on factuality hallucination in large language models
Junyi Li, Jie Chen, Ruiyang Ren, Xiaoxue Cheng, Wayne Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen · 2024
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Siya Qi, Yulan He, and Zheng Yuan · 2024
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Exclusive: Chatgpt traffic slips again for third month in a row
Reuters · 2024
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A comprehensive survey of hallucination mitigation techniques in large language models
SM Tonmoy, SM Zaman, Vinija Jain, Anku Rani, Vipula Rawte, Aman Chadha, and Amitava Das · 2024
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Pranav Narayanan Venkit, Tatiana Chakravorti, Vipul Gupta, Heidi Biggs, Mukund Srinath, Koustava Goswami, Sarah Rajtmajer, and Shomir Wilson · 2024
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Is rlhf more difficult than standard rl? a theoretical perspective
Yuanhao Wang, Qinghua Liu, and Chi Jin · 2024
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