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dc.contributor.advisorBethard, Steven
dc.contributor.advisorThomer, Andrea
dc.contributor.authorSong, Hyunju
dc.creatorSong, Hyunju
dc.date.accessioned2024-06-06T01:12:34Z
dc.date.available2024-06-06T01:12:34Z
dc.date.issued2024
dc.identifier.citationSong, Hyunju. (2024). Metadata Enhancement Using Large Language Models: Improving the Quality of Aggregated Records in the iSamples Project (Master's thesis, University of Arizona, Tucson, USA).
dc.identifier.urihttp://hdl.handle.net/10150/672555
dc.description.abstractAggregating resources from diverse data repositories often results in an incomplete ag- gregation despite curation efforts in the digital library space. To address this challenge, in this work we propose a method for automatic metadata assignment that is applicable across different metadata aggregations. Our approach uses zero and few-shot methods which harness the knowledge and reasoning abilities of large language models, while also incorporating the hierarchical structure of the taxonomy. We also illustrate how our approach can be integrated within a digital infrastructure in practical settings.
dc.language.isoen
dc.publisherThe University of Arizona.
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.titleMetadata Enhancement Using Large Language Models: Improving the Quality of Aggregated Records in the iSamples Project
dc.typeElectronic Thesis
dc.typetext
thesis.degree.grantorUniversity of Arizona
thesis.degree.levelmasters
dc.contributor.committeememberRiloff, Ellen
dc.contributor.committeememberBlanco, Edurado
thesis.degree.disciplineGraduate College
thesis.degree.disciplineComputer Science
thesis.degree.nameM.S.
refterms.dateFOA2024-06-06T01:12:34Z


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