PROTOCOL: HOW TO CORRECT THE CLASSIFICATION ERROR BY ASKING TO LARGE LANGUAGE MODELS THE SIMILARITY AMONG CATEGORIES

Giulio Giacomo Cantone

No d9egt, OSF Preprints from Center for Open Science

Abstract: Similarity between two categories is a number between 0 and 1 that abstractally represent how much the two categories overlap, objectively or subjectively. When two categories overlap, the error of classification of one to other is less severe. For example, misclassifying a wolf for dog is a less severe error than misclassifying a wolf for a cat, because wolf are more similar to dogs than cats. Nevertheless, canonical estimation of matrices of similarities for taxonomies of categories is expensive. In this protocol it is suggested why and how to estimate a similarity matrix from one or multiple Large Language Models.

Date: 2024-06-06
New Economics Papers: this item is included in nep-cmp
References: Add references at CitEc
Citations:

Downloads: (external link)
https://osf.io/download/6661e8706b6c8e272004d6c3/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:d9egt

DOI: 10.31219/osf.io/d9egt

Access Statistics for this paper

More papers in OSF Preprints from Center for Open Science
Bibliographic data for series maintained by OSF ().