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Protocol: How To Correct The Classification Error By Asking To Large Language Models The Similarity Among Categories

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  • Cantone, Giulio Giacomo

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.

Suggested Citation

  • Cantone, Giulio Giacomo, 2024. "Protocol: How To Correct The Classification Error By Asking To Large Language Models The Similarity Among Categories," OSF Preprints d9egt, Center for Open Science.
  • Handle: RePEc:osf:osfxxx:d9egt
    DOI: 10.31219/osf.io/d9egt
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