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Large Language Models (LLMs) are increasingly used for accessing information on the web.
Trust in automation: Designing for appropriate reliance
John D. Lee and Katrina A. See. 2004 · 2004
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Fact checking: Task definition and dataset construction
Andreas Vlachos and Sebastian Riedel. 2014 · 2014
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The role of explanations on trust and reliance in clinical decision support systems
Adrian Bussone, Simone Stumpf, and Dympna O’Sullivan. 2015 · 2015
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Partisanship, propaganda, and disinformation: Online media and the 2016 U.S. presidential election
Robert Faris, Hal Roberts, Bruce Etling, Nikki Bourassa, Ethan Zuckerman, and Yochai Benkler. 2017 · 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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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger. 2017 · 2017
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Troops, trolls and troublemakers: A global inventory of organized social media manipulation
Ricardo Mendes. 2017 · 2017
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller. 2017 · 2017
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Rationalization: A neural machine translation approach to generating natural language explanations
Upol Ehsan, Brent Harrison, Larry Chan, and Mark O. Riedl. 2018 · 2018
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Credeye: A credibility lens for analyzing and explaining misinformation
Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum. 2018a · 2018
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DeClarE: Debunking fake news and false claims using evidence-aware deep learning
Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, and Gerhard Weikum. 2018b · 2018
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Automated fact checking: Task formulations, methods and future directions
James Thorne and Andreas Vlachos. 2018 · 2018
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FEVER: a large-scale dataset for fact extraction and VERification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018 · 2018
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Examining the digital toolsets of journalists reporting on disinformation
Andrew Beers. 2019 · 2019
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What can AI do for me? Evaluating machine learning interpretations in cooperative play
Shi Feng and Jordan Boyd-Graber. 2019 · 2019
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Understanding the effect of accuracy on trust in machine learning models
Ming Yin, Jennifer Wortman Vaughan, and Hanna M. Wallach. 2019 · 2019
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Generating fact checking briefs
Angela Fan, Aleksandra Piktus, Fabio Petroni, Guillaume Wenzek, Marzieh Saeidi, Andreas Vlachos, Antoine Bordes, and Sebastian Riedel. 2020 · 2020
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QED: A framework and dataset for explanations in question answering
Matthew Lamm, Jennimaria Palomaki, Chris Alberti, Daniel Andor, Eunsol Choi, Livio Baldini Soares, and Michael Collins. 2020 · 2020
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Kuttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2020 · 2020
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Fact or fiction: Verifying scientific claims
David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, and Hannaneh Hajishirzi. 2020 · 2020
Cited alongside, same era.
Effect of confidence and explanation on accuracy and trust calibration in ai-assisted decision making
Yunfeng Zhang, Qingzi Vera Liao, and Rachel K. E. Bellamy. 2020 · 2020
Cited alongside, same era.
Does explainable artificial intelligence improve human decision-making?
Yasmeen Alufaisan, Laura R. Marusich, Jonathan Z. Bakdash, Yan Zhou, and Murat Kantarcioglu. 2021 · 2021
Cited alongside, same era.
Does the whole exceed its parts? The effect of AI explanations on complementary team performance
Gagan Bansal, Tongshuang Sherry Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel S. Weld. 2020 · 2021
Cited alongside, same era.
To trust or to think: Cognitive forcing functions can reduce overreliance on ai in ai-assisted decision-making
Zana Buçinca, Maja Barbara Malaya, and Krzysztof Z. Gajos. 2021 · 2021
Cited alongside, same era.
When not to trust language models: Investigating effectiveness of parametric and non-parametric memories
Alex Mallen, Akari Asai, Victor Zhong, Rajarshi Das, Hannaneh Hajishirzi, and Daniel Khashabi. 2022 · 2022
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Large dual encoders are generalizable retrievers
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, and Yinfei Yang. 2022 · 2022
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Single-turn debate does not help humans answer hard reading-comprehension questions
Alicia Parrish, H. Trivedi, Ethan Perez, Angelica Chen, Nikita Nangia, Jason Phang, and Sam Bowman. 2022 · 2022
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Understanding the role of explanation modality in AI-assisted decision-making
Vincent Robbemond, Oana Inel, and Ujwal Gadiraju. 2022 · 2022
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Human interpretation of saliency-based explanation over text
Hendrik Schuff, Alon Jacovi, Heike Adel, Yoav Goldberg, and Ngoc Thang Vu. 2022 · 2022
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You’d better stop! understanding human reliance on machine learning models under covariate shift
Chun-Wei Chiang and Ming Yin. 2021 · 2021
Cited alongside, same era.
Fool me twice: Entailment from Wikipedia gamification
Julian Eisenschlos, Bhuwan Dhingra, Jannis Bulian, Benjamin Börschinger, and Jordan Boyd-Graber. 2021 · 2021
Cited alongside, same era.
A survey on automated fact-checking
Zhijiang Guo, M. Schlichtkrull, and Andreas Vlachos. 2021 · 2021
Cited alongside, same era.
Towards a science of human-AI decision making: A survey of empirical studies
Vivian Lai, Chacha Chen, Qingzi Vera Liao, Alison Smith-Renner, and Chenhao Tan. 2021 · 2021
Cited alongside, same era.
Machine learning explanations to prevent overtrust in fake news detection
Sina Mohseni, Fan Yang, Shiva K. Pentyala, Mengnan Du, Yi Liu, Nic Lupfer, Xia Hu, Shuiwang Ji, and Eric D. Ragan. 2021 · 2021
Cited alongside, same era.
Automated fact-checking for assisting human fact-checkers
Preslav Nakov, David P. A. Corney, Maram Hasanain, Firoj Alam, Tamer Elsayed, Alberto Barr’on-Cedeno, Paolo Papotti, Shaden Shaar, and Giovanni Da San Martino. 2021 · 2021
Cited alongside, same era.
Manipulating and measuring model interpretability
Forough Poursabzi-Sangdeh, Daniel G Goldstein, Jake M Hofman, Jennifer Wortman Wortman Vaughan, and Hanna Wallach. 2021 · 2021
Cited alongside, same era.
Human-Centered Artificial Intelligence
Ben Shneiderman. 2022 · 2022
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Re-examining calibration: The case of question answering
Chenglei Si, Chen Zhao, Sewon Min, and Jordan L. Boyd-Graber. 2022 · 2022
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Explanations can reduce overreliance on AI systems during decision-making
Helena Vasconcelos, Matthew Jörke, Madeleine Grunde-McLaughlin, Tobias Gerstenberg, Michael Bernstein, and Ranjay Krishna. 2022 · 2022
Later among the works it cites.
Complex claim verification with evidence retrieved in the wild
Jifan Chen, Grace Kim, Aniruddh Sriram, Greg Durrett, and Eunsol Choi. 2023 · 2023
Closest in time.
Raymond Fok and Daniel S Weld. 2023 · 2023
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Enabling large language models to generate text with citations
Tianyu Gao, Howard Yen, Jiatong Yu, and Danqi Chen. 2023a · 2023
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What else do I need to know? The effect of background information on users’ reliance on QA systems
Navita Goyal, Eleftheria Briakou, Amanda Liu, Connor Baumler, Claire Bonial, Jeffrey Micher, Clare Voss, Marine Carpuat, and Hal Daumé III. 2023 · 2023
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Are Machine Rationales (Not) Useful to Humans? Measuring and Improving Human Utility of Free-text Rationales
Brihi Joshi, Ziyi Liu, Sahana Ramnath, Aaron Chan, Zhewei Tong, Shaoliang Nie, Qifan Wang, Yejin Choi, and Xiang Ren. 2023 · 2023
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WiCE: Real-world entailment for claims in Wikipedia
Ryo Kamoi, Tanya Goyal, Juan Diego Rodriguez, and Greg Durrett. 2023 · 2023
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Evaluating verifiability in generative search engines
Nelson Liu, Tianyi Zhang, and Percy Liang. 2023 · 2023
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Debate helps supervise unreliable experts
Julian Michael, Salsabila Mahdi, David Rein, Jackson Petty, Julien Dirani, Vishakh Padmakumar, and Samuel R. Bowman. 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 Hanna Hajishirzi. 2023 · 2023
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On the risk of misinformation pollution with large language models
Yikang Pan, Liangming Pan, Wenhu Chen, Preslav Nakov, Min-Yen Kan, and William Yang Wang. 2023 · 2023
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Understanding uncertainty: How lay decision-makers perceive and interpret uncertainty in human-ai decision making
Snehal Prabhudesai, Leyao Yang, Sumit Asthana, Xun Huan, Qingzi Vera Liao, and Nikola Banovic. 2023 · 2023
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