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Large language models are increasingly capable of generating fluent-appearing text with relatively little task-specific supervision.
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 · 1901
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The proof and measurement of association between two things
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Goal-based explanation evaluation
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Explanation as orgasm
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Explaining question answering models through text generation
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WT5?! training text-to-text models to explain their predictions
Sharan Narang, Colin Raffel, Katherine Lee, Adam Roberts, Noah Fiedel, and Karishma Malkan. 2020 · 2004
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Simplicity and probability in causal explanation
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A large annotated corpus for learning natural language inference
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Adam: A method for stochastic optimization
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Generating visual explanations
Lisa Anne Hendricks, Zeynep Akata, Marcus Rohrbach, Jeff Donahue, Bernt Schiele, and Trevor Darrell. 2016 · 2016
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Been Kim, Rajiv Khanna, and Oluwasanmi O Koyejo. 2016 · 2016
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Rationalizing neural predictions
Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2016 · 2016
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Evaluating everyday explanations
Jeffrey C Zemla, Steven Sloman, Christos Bechlivanidis, and David A Lagnado. 2017 · 2017
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e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
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Explainable autonomy: A study of explanation styles for building clear mental models
Francisco Javier Chiyah Garcia, David A. Robb, Xingkun Liu, Atanas Laskov, Pedro Patron, and Helen Hastie. 2018 · 2018
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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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Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018 · 2018
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Textual Explanations for Self-Driving Vehicles
Jinkyu Kim, Anna Rohrbach, Trevor Darrell, John F. Canny, and Zeynep Akata. 2018 · 2018
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Multimodal explanations: Justifying decisions and pointing to the evidence
Dong Huk Park, Lisa Anne Hendricks, Zeynep Akata, Anna Rohrbach, Bernt Schiele, Trevor Darrell, and Marcus Rohrbach. 2018 · 2018
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Hypothesis only baselines in natural language inference
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018 · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern. 2018 · 2018
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Automated rationale generation: A technique for explainable ai and its effects on human perceptions
Upol Ehsan, Pradyumna Tambwekar, Larry Chan, Brent Harrison, and Mark O Riedl. 2019 · 2019
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Are we modeling the task or the annotator? an investigation of annotator bias in natural language understanding datasets
Mor Geva, Yoav Goldberg, and Jonathan Berant. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
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Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in ai
Alon Jacovi, Ana Marasović, Tim Miller, and Yoav Goldberg. 2021 · 2021
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e-vil: A dataset and benchmark for natural language explanations in vision-language tasks
Maxime Kayser, Oana-Maria Camburu, Leonard Salewski, Cornelius Emde, Virginie Do, Zeynep Akata, and Thomas Lukasiewicz. 2021 · 2021
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Datasets: A community library for natural language processing
Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, Abhishek Thakur, Patrick von Platen, Suraj Patil, Julien Chaumond, Mariama Drame, Julien Plu, Lewis Tunstall, Joe Davison, Mario Šaško, Gunjan Chhablani, Bhavitvya Malik, Simon Brandeis, Teven Le Scao, Victor Sanh, Canwen Xu, Nicolas Patry, Angelina McMillan-Major, Philipp Schmid, Sylvain Gugger, Clément Delangue, Théo Matussière, Lysandre Debut, Stas Bekman, Pierric Cistac, Thibault Goehringer, Victor Mustar, François Lagunas, Alexander Rush, and Thomas Wolf. 2021 · 2021
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
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The curious case of neural text degeneration
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How decoding strategies affect the verifiability of generated text
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Calibrate before use: Improving few-shot performance of language models
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Fine-tuning language models from human preferences
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Can language models learn from explanations in context?
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Wanli: Worker and ai collaboration for natural language inference dataset creation
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
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Multitask prompted training enables zero-shot task generalization
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