Text embedding models yield detailed conceptual knowledge maps derived from short multiple-choice quizzes
Abstract Real-world conceptual knowledge is complex, multifaceted, and substantially over-simplified in most laboratory studies. Here we develop a mathematical framework, based on natural language processing models, for tracking and characterizing the acquisition of real-world conceptual knowledge....
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| Hoofdauteurs: | , , |
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| Formaat: | Artigo |
| Taal: | Inglês |
| Gepubliceerd in: |
Nature Portfolio
2026-03-01
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| Reeks: | Nature Communications |
| Online toegang: | https://doi.org/10.1038/s41467-026-69746-w |
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