<?xml version="1.0" encoding="UTF-8"?><oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/"><dc:title>language-models-are-few-shot-learners-2025-004</dc:title><dc:creator>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 Henig</dc:creator><dc:date>2025</dc:date><dc:description>This record links to the GPT-3 paper, which evaluates large autoregressive language models under few-shot, one-shot, and zero-shot settings. It is useful for stress-testing long author metadata, AI research keywords, external document display, and abstract search.</dc:description><dc:identifier>https://inrepscholar.com/projects/943</dc:identifier></oai_dc:dc>