Text embeddings recover 57-63% of the reliable variance in exam-item difficulty, and apparent differences in predictability across IRT parameters are mostly artifacts of calibration noise rather than text signal.
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2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 2years
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MAFIG is a multi-agent framework that uses LLM agents and evaluators to generate reading comprehension items with significantly higher adherence to specified feature constraints than single-agent baselines.
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From Text to Parameters: Predicting Item Parameters from Embedding Regularization with Reliability and Design Ceilings
Text embeddings recover 57-63% of the reliable variance in exam-item difficulty, and apparent differences in predictability across IRT parameters are mostly artifacts of calibration noise rather than text signal.
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A Multi-Agent Framework for Feature-Constrained Difficulty Control in Reading Comprehension Item Generation
MAFIG is a multi-agent framework that uses LLM agents and evaluators to generate reading comprehension items with significantly higher adherence to specified feature constraints than single-agent baselines.