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The rise of powerful large language models (LLMs) brings about tremendous opportunities for innovation but also looming risks for individuals and society at large.
Verification of forecasts expressed in terms of probability
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The mindlessness of ostensibly thoughtful action: The role of “placebic” information in interpersonal interaction
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Mental Models: Towards a Cognitive Science of Language, Inference, and Consciousness
Philip Johnson-Laird · 1983
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Some observations on mental models
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Conversational processes and causal explanation
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Machines and mindlessness: Social responses to computers
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Rouge: A package for automatic evaluation of summaries
Chin-Yew Lin · 2004
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Explanation and understanding
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How the mind explains behavior: Folk explanations, meaning, and social interaction
Bertram F Malle · 2006
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Computer anxiety and anger: The impact of computer use, computer experience, and self-efficacy beliefs
Jeffery D Wilfong · 2006
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Complacency and bias in human use of automation: An attentional integration
Raja Parasuraman and Dietrich H Manzey · 2010
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Tell me more? the effects of mental model soundness on personalizing an intelligent agent
Todd Kulesza, Simone Stumpf, Margaret Burnett, and Irwin Kwan · 2012
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Explanation and abductive inference
Tania Lombrozo · 2012
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Understanding the complex dynamics of transparency
Albert Meijer · 2013
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Power to the people: The role of humans in interactive machine learning
Saleema Amershi, Maya Cakmak, William Bradley Knox, and Todd Kulesza · 2014
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Some observations on mental models
Donald A Norman · 2014
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Auditing algorithms: Research methods for detecting discrimination on internet platforms
Christian Sandvig, Kevin Hamilton, Karrie Karahalios, and Cedric Langbort · 2014
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
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Complacency and automation bias in the use of imperfect automation
Christopher D Wickens, Benjamin A Clegg, Alex Z Vieane, and Angelia L Sebok · 2015
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First I “like” it, then I hide it: Folk theories of social feeds
Motahhare Eslami, Karrie Karahalios, Christian Sandvig, Kristen Vaccaro, Aimee Rickman, Kevin Hamilton, and Alex Kirlik · 2016
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When (ish) is my bus? user-centered visualizations of uncertainty in everyday, mobile predictive systems
Matthew Kay, Tara Kola, Jessica R Hullman, and Sean A Munson · 2016
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Examples are not enough, learn to criticize! criticism for interpretability
Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo · 2016
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola · 2016
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Explanatory preferences shape learning and inference
Tania Lombrozo · 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
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Deep reinforcement learning from human preferences
Paul F. Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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A unified approach to interpreting model predictions
Scott Lundberg and Su-In Lee · 2017
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Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J. Liu, and Christopher D. Manning · 2017
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Machine teaching: A new paradigm for building machine learning systems
Patrice Y Simard, Saleema Amershi, David M Chickering, Alicia Edelman Pelton, Soroush Ghorashi, Christopher Meek, Gonzalo Ramos, Jina Suh, Johan Verwey, Mo Wang, et al · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability
Mike Ananny and Kate Crawford · 2018
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Data statements for natural language processing: Toward mitigating system bias and enabling better science
Emily M. Bender and Batya Friedman · 2018
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Bringing transparency design into practice
Malin Eiband, Hanna Schneider, Mark Bilandzic, Julian Fazekas-Con, Mareike Haug, and Heinrich Hussmann · 2018
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Pathologies of neural models make interpretations difficult
Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, and Jordan Boyd-Graber · 2018
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Uncertainty displays using quantile dotplots or cdfs improve transit decision-making
Michael Fernandes, Logan Walls, Sean Munson, Jessica Hullman, and Matthew Kay · 2018
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The dataset nutrition label: A framework to drive higher data quality standards
Sarah Holland, Ahmed Hosny, Sarah Newman, Joshua Joseph, and Kasia Chmielinski · 2018
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Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder · 2018
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Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, et al · 2018
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Improving language understanding with unsupervised learning
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever · 2018
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Neural-symbolic vqa: Disentangling reasoning from vision and language understanding
Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, and Josh Tenenbaum · 2018
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Factsheets: Increasing trust in AI services through supplier’s declarations of conformity
Matthew Arnold, Rachel KE Bellamy, Michael Hind, Stephanie Houde, Sameep Mehta, Aleksandra Mojsilović, Ravi Nair, K Natesan Ramamurthy, Alexandra Olteanu, David Piorkowski, et al · 2019
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Resilient chatbots: Repair strategy preferences for conversational breakdowns
Zahra Ashktorab, Mohit Jain, Q. Vera Liao, and Justin D Weisz · 2019
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Machine learning interpretability: A survey on methods and metrics
Diogo V Carvalho, Eduardo M Pereira, and Jaime S Cardoso · 2019
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Confidence scoring using whitebox meta-models with linear classifier probes
Tongfei Chen, Jirí Navrátil, Vijay Iyengar, and Karthikeyan Shanmugam · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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The impact of placebic explanations on trust in intelligent systems
Malin Eiband, Daniel Buschek, Alexander Kremer, and Heinrich Hussmann · 2019
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Neural module networks for reasoning over text
Nitish Gupta, Kevin Lin, Dan Roth, Sameer Singh, and Matt Gardner · 2019
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Attention is not Explanation
Sarthak Jain and Byron C. Wallace · 2019
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Ctrl: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R Varshney, Caiming Xiong, and Richard Socher · 2019
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Procedural justice in algorithmic fairness: Leveraging transparency and outcome control for fair algorithmic mediation
Min Kyung Lee, Anuraag Jain, Hea Jin Cha, Shashank Ojha, and Daniel Kusbit · 2019
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Explanation in artificial intelligence: Insights from the social sciences
Tim Miller · 2019
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Cynthia Rudin · 2019
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Efficient search for diverse coherent explanations
Chris Russell · 2019
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Actionable recourse in linear classification
Berk Ustun, Alexander Spangher, and Yang Liu · 2019
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Communicating uncertainty about facts, numbers and science
Anne Marthe Van Der Bles, Sander Van Der Linden, Alexandra LJ Freeman, James Mitchell, Ana B Galvao, Lisa Zaval, and David J Spiegelhalter · 2019
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Attention is not not explanation
Sarah Wiegreffe and Yuval Pinter · 2019
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Errudite: Scalable, reproducible, and testable error analysis
Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, and Daniel S Weld · 2019
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Understanding the effect of accuracy on trust in machine learning models
Ming Yin, Jennifer Wortman Vaughan, and Hanna Wallach · 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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Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi · 2019
Cited alongside, same era.
Jasmijn Bastings and Katja Filippova · 2020
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Explainable machine learning in deployment
Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José MF Moura, and Peter Eckersley · 2020
Cited alongside, same era.
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
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Teachable machine: Approachable web-based tool for exploring machine learning classification
Michelle Carney, Barron Webster, Irene Alvarado, Kyle Phillips, Noura Howell, Jordan Griffith, Jonas Jongejan, Amit Pitaru, and Alexander Chen · 2020
Ethical and social risks of harm from language models
Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, et al · 2021
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Artificial fingerprinting for generative models: Rooting deepfake attribution in training data
Ning Yu, Vladislav Skripniuk, Sahar Abdelnabi, and Mario Fritz · 2021
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ChatGPT and how AI disrupts industries
Ajay Agrawal, Joshua Gans, and Avi Goldfarb · 2022
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The effects of system initiative during conversational collaborative search
Sandeep Avula, Bogeum Choi, and Jaime Arguello · 2022
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Constitutional AI: Harmlessness from AI feedback
Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al · 2022
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A survey of the state of explainable AI for natural language processing
Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, and Prithviraj Sen · 2020
Cited alongside, same era.
What bert is not: Lessons from a new suite of psycholinguistic diagnostics for language models
Allyson Ettinger · 2020
Cited alongside, same era.
Towards transparency by design for artificial intelligence
Heike Felzmann, Eduard Fosch-Villaronga, Christoph Lutz, and Aurelia Tamò-Larrieux · 2020
Cited alongside, same era.
Mental models of AI agents in a cooperative game setting
Katy Ilonka Gero, Zahra Ashktorab, Casey Dugan, Qian Pan, James Johnson, Werner Geyer, Maria Ruiz, Sarah Miller, David R Millen, Murray Campbell, et al · 2020
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Human trust in artificial intelligence: Review of empirical research
Ella Glikson and Anita Williams Woolley · 2020
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Against scale: Provocations and resistances to scale thinking
Alex Hanna and Tina M Park · 2020
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2020
Cited alongside, same era.
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Interactive model cards: A human-centered approach to model documentation
Anamaria Crisan, Margaret Drouhard, Jesse Vig, and Nazneen Rajani · 2022
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How generative AI is changing creative work
Thomas H. Davenport and Nitin Mittal · 2022
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Communicating uncertainty using words and numbers
Mandeep K Dhami and David R Mandel · 2022
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Predictability and surprise in large generative models
Deep Ganguli, Danny Hernandez, Liane Lovitt, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova Dassarma, Dawn Drain, Nelson Elhage, et al · 2022
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Repairing the cracked foundation: A survey of obstacles in evaluation practices for generated text
Sebastian Gehrmann, Elizabeth Clark, and Thibault Sellam · 2022
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Neo: Generalizing confusion matrix visualization to hierarchical and multi-output labels
Jochen Görtler, Fred Hohman, Dominik Moritz, Kanit Wongsuphasawat, Donghao Ren, Rahul Nair, Marc Kirchner, and Kayur Patel · 2022
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Attitudes and folk theories of data subjects on transparency and accuracy in emotion recognition
Gabriel Grill and Nazanin Andalibi · 2022
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Improving understandability of feature contributions in model-agnostic explainable AI tools
Sophia Hadash, Martijn C Willemsen, Chris Snijders, and Wijnand A IJsselsteijn · 2022
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Understanding machine learning practitioners’ data documentation perceptions, needs, challenges, and desiderata
Amy K. Heger, Liz B. Marquis, Mihaela Vorvoreanu, Hanna Wallach, and Jennifer Wortman Vaughan · 2022
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Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Oriol Vinyals, Jack W. Rae, and L. Sifre · 2022
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Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung · 2022
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Language models (mostly) know what they know
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, Scott Johnston, Sheer El-Showk, Andy Jones, Nelson Elhage, Tristan Hume, Anna Chen, Yuntao Bai, Sam Bowman, Stanislav Fort, Deep Ganguli, Danny Hernandez, Josh Jacobson, Jackson Kernion, Shauna Kravec, Liane Lovitt, Kamal Ndousse, Catherine Olsson, Sam Ringer, Dario Amodei, Tom Brown, Jack Clark, Nicholas Joseph, Ben Mann, Sam McCandlish, Chris Olah, and Jared Kaplan · 2022
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The subjects and stages of AI dataset development: A framework for dataset accountability
Mehtab Khan and Alex Hanna · 2022
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Explainable AI: Another successful failure?
Bran Knowles · 2022
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All the news that’s ft to fabricate: AI-generated text as a tool of media misinformation
Sarah Kreps, R Miles McCain, and Miles Brundage · 2022
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Large language models with controllable working memory
Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, and Sanjiv Kumar · 2022
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Holistic evaluation of language models
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Designing for responsible trust in AI systems: A communication perspective
Q. Vera Liao and S Shyam Sundar · 2022
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Connecting algorithmic research and usage contexts: A perspective of contextualized evaluation for explainable ai
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The conflict between explainable and accountable decision-making algorithms
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TruthfulQA: Measuring how models mimic human falsehoods
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What makes good in-context examples for GPT-3?
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Towards faithful model explanation in nlp: A survey
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Post-hoc interpretability for neural NLP: A survey
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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
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When confidence meets accuracy: Exploring the effects of multiple performance indicators on trust in machine learning models
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A survey of evaluation metrics used for nlg systems
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Bloom: A 176b-parameter open-access multilingual language model
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