<?xml version="1.0" encoding="UTF-8"?><resource xmlns="http://datacite.org/schema/kernel-4"><identifier identifierType="URL">https://inrepscholar.com/projects/943</identifier><creators><creator><creatorName>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</creatorName></creator></creators><titles><title>language-models-are-few-shot-learners-2025-004</title></titles><publisher>UFTAN UNIVERSITY</publisher><publicationYear>2025</publicationYear><resourceType resourceTypeGeneral="Text">Final Year Project</resourceType><descriptions><description descriptionType="Abstract">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.</description></descriptions></resource>