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Pre-trained language models learn socially harmful biases from their training corpora, and may repeat these biases when used for generation.
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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Prominent characters and events organize narrative understanding
Daniel G. Morrow. 1985 · 1985
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Efficient estimation of word representations in vector space
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Semantics derived automatically from language corpora necessarily contain human biases
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BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
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Austin C Kozlowski, Matt Taddy, and James A Evans. 2019 · 2019
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