SEMbeddings: how to evaluate model misfit before data collection using large-language models.

Journal: Frontiers in psychology
Published Date: Feb 4, 2025

Abstract

INTRODUCTION: Recent developments suggest that Large Language Models (LLMs) provide a promising approach for approximating empirical correlation matrices of item responses by utilizing item embeddings and their cosine similarities. In this paper, we introduce a novel tool, which we label .

Authors

  • Tommaso Feraco
    Department of General Psychology, University of Padova, Padua, Italy.
  • Enrico Toffalini
    Department of General Psychology, University of Padova, Padua, Italy.

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