Topic: GPT-3

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This page shows the most relevant public items for GPT-3, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.

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  1. Language Models are Few-Shot Learners

    Paper • May 28, 2020 • arXiv • 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 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, Dario Amodei

    Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic i...

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It ranks public content for GPT-3 using recent discussion, review, and engagement signals so you can triage faster. This guidance is specific to GPT-3 topic page on Attendemia and is written so it still makes sense without reading other sections on the page.

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Use weekly for fast-moving updates, monthly for stable trend confirmation, and all-time for evergreen references. This guidance is specific to GPT-3 topic page on Attendemia and is written so it still makes sense without reading other sections on the page.

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