<?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>chain-of-thought-prompting-elicits-reasoning-in-large-language-models-2025-013</dc:title><dc:creator>Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed H. Chi, Quoc V. Le, Denny Zhou</dc:creator><dc:date>2025</dc:date><dc:description>This record links to the chain-of-thought prompting paper, which studies how intermediate reasoning steps improve large language model performance. It is useful for testing education technology records and AI-assisted learning metadata.</dc:description><dc:identifier>https://inrepscholar.com/projects/952</dc:identifier></oai_dc:dc>