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In everyday conversations, humans can take on different roles and adapt their vocabulary to their chosen roles.
A formula for predicting readability
Edgar Dale and Jeanne Sternlicht Chall · 1948
Earlier work this paper cites.
The technique of clear writing
Robbie Gunning · 1968
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Smog grading - a new readability formula
G. Harry Mclaughlin · 1969
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Assessing readability
George R. Klare · 1974
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Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel
J. Peter Kincaid, Robert P. Fishburne, Richard L. Rogers, and Brad S. Chissom · 1975
Earlier work this paper cites.
A computer readability formula designed for machine scoring
Meri Coleman and Ta Lin Liau · 1975
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Readability revisited : the new dale-chall readability formula
Jeanne Sternlicht Chall and Edgar Dale · 1995
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Cognitive and language development in children
John Ed Oates and Andrew Ed Grayson · 2004
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge J. Belongie · 2011
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan · 2017
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spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani · 2017
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Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly
Yongqin Xian, Christoph H Lampert, Bernt Schiele, and Zeynep Akata · 2018
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Unsupervised text style transfer using language models as discriminators
Zichao Yang, Zhiting Hu, Chris Dyer, Eric P Xing, and Taylor Berg-Kirkpatrick · 2018
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Deconstructing the human algorithms for exploration
Samuel J Gershman · 2018
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Ctrl: A conditional transformer language model for controllable generation
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The algorithmic architecture of exploration in the human brain
Eric Schulz and Samuel J Gershman · 2019
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Reinforcement learning across development: What insights can we draw from a decade of research?
Kate Nussenbaum and Catherine A Hartley · 2019
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Searching for rewards like a child means less generalization and more directed exploration
Eric Schulz, Charley M Wu, Azzurra Ruggeri, and Björn Meder · 2019
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Systematic exploration and uncertainty dominate young children’s choices
Nathaniel J. Blanco and Vladimir M. Sloutsky · 2019
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Development of vocabulary sophistication across genres in english children’s writing
Philip Durrant and Mark Brenchley · 2019
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Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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
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On extractive and abstractive neural document summarization with transformer language models
Jonathan Pilault, Raymond Li, Sandeep Subramanian, and Christopher Pal · 2020
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Can gpt-3 pass a writer’s turing test?
Katherine Elkins and Jon Chun · 2020
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Generalizing from a few examples: A survey on few-shot learning
Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni · 2020
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AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh · 2020
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Children are more exploratory and learn more than adults in an approach-avoid task
Emily G. Liquin and Alison Gopnik · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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On the opportunities and risks of foundation models
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Understanding the capabilities, limitations, and societal impact of large language models
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On the dangers of stochastic parrots: Can language models be too big?
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Exploiting cloze-questions for few-shot text classification and natural language inference
Developmental changes in learning resemble stochastic optimization
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Palm: Scaling language modeling with pathways
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Training compute-optimal large language models
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Laion-5b: An open large-scale dataset for training next generation image-text models
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Laria Reynolds and Kyle McDonell · 2021
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Persistent anti-muslim bias in large language models
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