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This paper proposes an evaluation framework for predicting item parameters from text embeddings using regularized regression and reliability/design ceilings. Results show that difficulty is substantially predictable from text, while discrimination and pseudo-guessing are limited by reliability, not text signal.
Proposes Trans-Ising, a transfer learning method for high-dimensional Ising models that uses a loss-based source screening rule and two-stage estimation to improve estimation accuracy over target-only and naive pooling methods.