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Large-scale generative models enabled the development of AI-powered code completion tools to assist programmers in writing code.
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 · 1901
Earlier work this paper cites.
A neural probabilistic language model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003 · 2003
Earlier work this paper cites.
Predicting good probabilities with supervised learning. In Proceedings of the 22nd international conference on Machine learning . 625–632
Alexandru Niculescu-Mizil and Rich Caruana. 2005 · 2005
Earlier work this paper cites.
NASA-task load index (NASA-TLX); 20 years later. In Proceedings of the human factors and ergonomics society annual meeting , Vol. 50. Sage publications Sage CA: Los Angeles, CA, 904–908
Sandra G Hart. 2006 · 2006
Earlier work this paper cites.
Estimating residual error rate in recognized handwritten documents using artificial error injection. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems . 1–4
Edward Lank, Ryan Stedman, and Michael Terry. 2010 · 2010
Earlier work this paper cites.
Complacency and bias in human use of automation: An attentional integration
Raja Parasuraman and Dietrich H Manzey. 2010 · 2010
Earlier work this paper cites.
The world’s leading online programming learning platform
LeetCode. 2015 · 2015
Earlier work this paper cites.
https://beta.openai.com/playground
OpenAI. 2015 · 2015
Earlier work this paper cites.
Complacency and automation bias in the use of imperfect automation
Christopher D Wickens, Benjamin A Clegg, Alex Z Vieane, and Angelia L Sebok. 2015 · 2015
Earlier work this paper cites.
Machine bias: There’s software across the country to predict future criminals and it’s biased against blacks
Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. 2016 · 2016
Earlier work this paper cites.
On calibration of modern neural networks. In International conference on machine learning . PMLR, 1321–1330
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017 · 2017
Earlier work this paper cites.
Can AI become reliable source to support human decision making in a court scene?. In Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing . 195–198
Yugo Hayashi and Kosuke Wakabayashi. 2017 · 2017
Earlier work this paper cites.
Effects of spell checkers on English as a second language students’ incidental spelling learning: a cognitive load perspective
Po-Han Lin, Tzu-Chien Liu, and Fred Paas. 2017 · 2017
Earlier work this paper cites.
Attention is All you Need. In Advances in Neural Information Processing Systems , Vol. 30
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Creative writing with a machine in the loop: Case studies on slogans and stories. In 23rd International Conference on Intelligent User Interfaces . 329–340
Elizabeth Clark, Anne Spencer Ross, Chenhao Tan, Yangfeng Ji, and Noah A Smith. 2018 · 2018
Earlier work this paper cites.
Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening
Cynthia CS Liem, Markus Langer, Andrew Demetriou, Annemarie MF Hiemstra, Achmadnoer Sukma Wicaksana, Marise Ph Born, and Cornelius J König. 2018 · 2018
Earlier work this paper cites.
Explainable machine-learning predictions for the prevention of hypoxaemia during surgery
Scott M Lundberg, Bala Nair, Monica S Vavilala, Mayumi Horibe, Michael J Eisses, Trevor Adams, David E Liston, Daniel King-Wai Low, Shu-Fang Newman, Jerry Kim, et al · 2018
Earlier work this paper cites.
" Hello AI": Uncovering the Onboarding Needs of Medical Practitioners for Human-AI Collaborative Decision-Making
Carrie J Cai, Samantha Winter, David Steiner, Lauren Wilcox, and Michael Terry. 2019 · 2019
Earlier work this paper cites.
Will You Accept an Imperfect AI?: Exploring Designs for Adjusting End-user Expectations of AI Systems
Rafal Kocielnik, Saleema Amershi, and Paul N. Bennett. 2019 · 2019
Earlier work this paper cites.
Identifying fluently inadequate output in neural and statistical machine translation. In Proceedings of Machine Translation Summit XVII Volume 1: Research Track . 233–243
Marianna Martindale, Marine Carpuat, Kevin Duh, and Paul McNamee. 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Earlier work this paper cites.
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 · 2019
Earlier work this paper cites.
Understanding the Effect of Accuracy on Trust in Machine Learning Models
Ming Yin, Jennifer Wortman Vaughan, and Hanna M. Wallach. 2019 · 2019
Earlier work this paper cites.
Algorithmic Risk Assessments Can Alter Human Decision-Making Processes in High-Stakes Government Contexts
Ben Green and Yiling Chen. 2020 · 2020
Earlier work this paper cites.
"Why is ’Chicago’ deceptive?" Towards Building Model-Driven Tutorials for Humans
Vivian Lai, Han Liu, and Chenhao Tan. 2020 · 2020
Cited alongside, same era.
Novice-AI Music Co-Creation via AI-Steering Tools for Deep Generative Models
Ryan Louie, Andy Coenen, Cheng-Zhi Anna Huang, Michael Terry, and Carrie J. Cai. 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 the whole exceed its parts? the effect of ai explanations on complementary team performance. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems . 1–16
Gagan Bansal, Tongshuang Wu, Joyce Zhou, Raymond Fok, Besmira Nushi, Ece Kamar, Marco Tulio Ribeiro, and Daniel Weld. 2021 · 2021
Cited alongside, same era.
On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT) . 610–623
Opal: Multimodal Image Generation for News Illustration
Vivian Liu, Han Qiao, and Lydia B. Chilton. 2022 · 2022
Later among the works it cites.
Reducing conversational agents’ overconfidence through linguistic calibration
Sabrina J Mielke, Arthur Szlam, Emily Dinan, and Y-Lan Boureau. 2022 · 2022
Later among the works it cites.
Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programming
Hussein Mozannar, Gagan Bansal, Adam Fourney, and Eric Horvitz. 2022 · 2022
Later among the works it cites.
Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions. In 2022 IEEE Symposium on Security and Privacy (SP) . 754–768
Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri. 2022 · 2022
Later among the works it cites.
Do users write more insecure code with AI assistants?
Neil Perry, Megha Srivastava, Deepak Kumar, and Dan Boneh. 2022 · 2022
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Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
Cited alongside, same era.
Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society . 401–413
Umang Bhatt, Javier Antorán, Yunfeng Zhang, Q Vera Liao, Prasanna Sattigeri, Riccardo Fogliato, Gabrielle Melançon, Ranganath Krishnan, Jason Stanley, Omesh Tickoo, et al · 2021
Cited alongside, same era.
Onboarding Materials as Cross-functional Boundary Objects for Developing AI Assistants
Carrie J. Cai, Samantha Winter, David F. Steiner, Lauren Wilcox, and Michael Terry. 2021 · 2021
Cited alongside, same era.
The SPACE of Developer Productivity: There’s more to it than you think
Nicole Forsgren, Margaret-Anne Storey, Chandra Maddila, Thomas Zimmermann, Brian Houck, and Jenna Butler. 2021 · 2021
Cited alongside, same era.
Do Explanations Help Users Detect Errors in Open-Domain QA? An Evaluation of Spoken vs. Visual Explanations. In Findings of ACL
Ana Valeria Gonzalez, Gagan Bansal, Angela Fan, Yashar Mehdad, Robin Jia, and Srini Iyer. 2021 · 2021
Cited alongside, same era.
How machine-learning recommendations influence clinician treatment selections: the example of antidepressant selection
Maia L. Jacobs, Melanie Fernandes Pradier, Thomas H. McCoy, Roy H. Perlis, Finale Doshi-Velez, and Krzysztof Z Gajos. 2021 · 2021
Cited alongside, same era.
How can we know when language models know? on the calibration of language models for question answering
Zhengbao Jiang, Jun Araki, Haibo Ding, and Graham Neubig. 2021 · 2021
Cited alongside, same era.
Teaching Humans When To Defer to a Classifier via Examplars. In AAAI
Hussein Mozannar, Arvindmani Satyanarayan, and David A. Sontag. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
What is it like to program with artificial intelligence?
Advait Sarkar, Andrew D Gordon, Carina Negreanu, Christian Poelitz, Sruti Srinivasa Ragavan, and Ben Zorn. 2022 · 2022
Later among the works it cites.
Self-critiquing models for assisting human evaluators
William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike. 2022 · 2022
Later among the works it cites.
Investigating Explainability of Generative AI for Code through Scenario-Based Design. In 27th International Conference on Intelligent User Interfaces (Helsinki, Finland) (IUI ’22) . Association for Computing Machinery, New York, NY, USA, 212–228
Jiao Sun, Q. Vera Liao, Michael Muller, Mayank Agarwal, Stephanie Houde, Kartik Talamadupula, and Justin D. Weisz. 2022 · 2022
Later among the works it cites.
Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models. In CHI Conference on Human Factors in Computing Systems Extended Abstracts . 1–7
Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman. 2022 · 2022
Later among the works it cites.
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.
Productivity Assessment of Neural Code Completion. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming (San Diego, CA, USA) (MAPS 2022) . Association for Computing Machinery, New York, NY, USA, 21–29
Albert Ziegler, Eirini Kalliamvakou, X. Alice Li, Andrew Rice, Devon Rifkin, Shawn Simister, Ganesh Sittampalam, and Edward Aftandilian. 2022 · 2022
Later among the works it cites.
Large Language Models and Simple, Stupid Bugs
Kevin Jesse, Toufique Ahmed, Premkumar T. Devanbu, and Emily Morgan. 2023 · 2023
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RU-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents
Daniel D Johnson, Daniel Tarlow, and Christian Walder. 2023 · 2023
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PAC Prediction Sets for Large Language Models of Code
Adam Khakhar, Stephen Mell, and Osbert Bastani. 2023 · 2023
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Methods to Estimate Large Language Model Confidence
Maia Kotelanski, Robert Gallo, Ashwin Nayak, and Thomas Savage. 2023 · 2023
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Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. 2023 · 2023
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Prudent Silence or Foolish Babble? Examining Large Language Models’ Responses to the Unknown
Genglin Liu, Xingyao Wang, Lifan Yuan, Yangyi Chen, and Hao Peng. 2023 · 2023
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From Copilot to Pilot: Towards AI Supported Software Development
Rohith Pudari and Neil A. Ernst. 2023 · 2023
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Llamas Know What GPTs Don’t Show: Surrogate Models for Confidence Estimation
Vaishnavi Shrivastava, Percy Liang, and Ananya Kumar. 2023 · 2023
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The Confidence-Competence Gap in Large Language Models: A Cognitive Study
Aniket Kumar Singh, Suman Devkota, Bishal Lamichhane, Uttam Dhakal, and Chandra Dhakal. 2023 · 2023
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Quantifying Uncertainty in Natural Language Explanations of Large Language Models
Sree Harsha Tanneru, Chirag Agarwal, and Himabindu Lakkaraju. 2023 · 2023
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AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023 · 2023
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Navigating the grey area: Expressions of overconfidence and uncertainty in language models
Kaitlyn Zhou, Dan Jurafsky, and Tatsunori Hashimoto. 2023 · 2023
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