SPIN Unprocessed July 9, 2026 ai_technology research
From Text to Parameters: Predicting Item Parameters from Embedding Regularization with Reliability and Design Ceilings
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arXiv:2607.07141v1 Announce Type: new Abstract: Newly developed items must ordinarily be field tested before their psychometric properties are known, creating a cold start problem for item calibration. Predicting item parameters from features is a long standing measurement problem dating back to the Linear Logistic Test Model; modern text embeddings now automate the design matrices traditionally specified by hand. We propose an evaluation framework combining regularized regression on item text e
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