![]() On-the-fly reasoning or domain adaptation, such as unscrambling words, using a ![]() Question-answering, and cloze tasks, as well as several tasks that require GPT-3Īchieves strong performance on many NLP datasets, including translation, For all tasks, GPT-3 isĪpplied without any gradient updates or fine-tuning, with tasks and few-shotĭemonstrations specified purely via text interaction with the model. ![]() Specifically, we train GPT-3, an autoregressive language model withġ75 billion parameters, 10x more than any previous non-sparse language model,Īnd test its performance in the few-shot setting. Language models greatly improves task-agnostic, few-shot performance, sometimesĮven reaching competitiveness with prior state-of-the-art fine-tuningĪpproaches. Task from only a few examples or from simple instructions - something whichĬurrent NLP systems still largely struggle to do. By contrast, humans can generally perform a new language Still requires task-specific fine-tuning datasets of thousands or tens of While typically task-agnostic in architecture, this method 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 Download PDF Abstract: Recent work has demonstrated substantial gains on many NLP tasks andīenchmarks by pre-training on a large corpus of text followed by fine-tuning onĪ specific task. 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.
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