<?xml version="1.0" encoding="UTF-8"?><resource xmlns="http://datacite.org/schema/kernel-4"><identifier identifierType="URL">https://inrepscholar.com/projects/952</identifier><creators><creator><creatorName>Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed H. Chi, Quoc V. Le, Denny Zhou</creatorName></creator></creators><titles><title>chain-of-thought-prompting-elicits-reasoning-in-large-language-models-2025-013</title></titles><publisher>UFTAN UNIVERSITY</publisher><publicationYear>2025</publicationYear><resourceType resourceTypeGeneral="Text">Masters Thesis</resourceType><descriptions><description descriptionType="Abstract">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.</description></descriptions></resource>