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A human decision-maker benefits the most from an AI assistant that corrects for their biases.
Ctrl: A conditional transformer language model for controllable generation
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Variations in physician practice: The role of uncertainty
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Resident uncertainty in clinical decision making and impact on patient care: a qualitative study
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From mindless to mindful practice — cognitive bias and clinical decision making
Pat Croskerry. 2013 · 2013
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Understanding and confronting our mistakes: The epidemiology of error in radiology and strategies for error reduction
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Diverse beam search: Decoding diverse solutions from neural sequence models
Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. 2016 · 2016
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Interpretive error in radiology
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Bias in radiology: The how and why of misses and misinterpretations
Lindsay P. Busby, Jesse L. Courtier, and Christine M. Glastonbury. 2018 · 2018
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Hierarchical neural story generation
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Understanding uncertainty in medicine: concepts and implications in medical education
Kangmoon Kim and Young-Mee Lee. 2018 · 2018
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Abductive commonsense reasoning
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Plug and play language models: A simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. 2020 · 2020
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
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Neural text generation with unlikelihood training
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2020 · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
Coda: Contrast-enhanced and diversity-promoting data augmentation for natural language understanding
Yanru Qu, Dinghan Shen, Yelong Shen, Sandra Sajeev, Weizhu Chen, and Jiawei Han. 2021 · 2021
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Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study
Jarrel C Y Seah, Cyril H M Tang, Quinlan D Buchlak, Xavier G Holt, Jeffrey B Wardman, Anuar Aimoldin, Nazanin Esmaili, Hassan Ahmad, Hung Pham, John F Lambert, Ben Hachey, Stephen J F Hogg, Benjamin P Johnston, Christine Bennett, Luke Oakden-Rayner, Peter Brotchie, and Catherine M Jones. 2021 · 2021
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FUDGE: Controlled text generation with future discriminators
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Cont: Contrastive neural text generation
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e-CARE: a new dataset for exploring explainable causal reasoning
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CLIFF: Contrastive learning for improving faithfulness and factuality in abstractive summarization
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SimCSE: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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Radbert-cl: Factually-aware contrastive learning for radiology report classification
Ajay Jaiswal, Liyan Tang, Meheli Ghosh, Justin F. Rousseau, Yifan Peng, and Ying Ding. 2021 · 2021
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Contrastive learning with adversarial perturbations for conditional text generation
Seanie Lee, Dong Bok Lee, and Sung Ju Hwang. 2021 · 2021
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Plug-and-blend: A framework for controllable story generation with blended control codes
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DExperts: Decoding-time controlled text generation with experts and anti-experts
Alisa Liu, Maarten Sap, Ximing Lu, Swabha Swayamdipta, Chandra Bhagavatula, Noah A. Smith, and Yejin Choi. 2021 · 2021
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Errors, discrepancies and underlying bias in radiology with case examples: a pictorial review
Omer Onder, Yasin Yarasir, Aynur Azizova, Gamze Durhan, Mehmet Ruhi Onur, and Orhan Macit Ariyurek. 2021 · 2021
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Li Du, Xiao Ding, Kai Xiong, Ting Liu, and Bing Qin. 2022 · 2022
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Contrastive decoding: Open-ended text generation as optimization
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Quark: Controllable text generation with reinforced unlearning
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Plug-and-play controller for story completion: A pilot study toward emotion-aware story writing assistance
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Biobart: Pretraining and evaluation of a biomedical generative language model
Hongyi Yuan, Zheng Yuan, Ruyi Gan, Jiaxing Zhang, Yutao Xie, and Sheng Yu. 2022 · 2022
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Discup: Discriminator cooperative unlikelihood prompt tuning for controllable text generation
Hanqing Zhang and Dawei Song. 2022 · 2022
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Training dynamics for text summarization models
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