A method using predicted rectification difficulty for optimal human sample allocation in LLM-augmented surveys captures 61-79% of theoretical efficiency gains and reduces MSE by 11% on two datasets without pilot data.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Introduces interval graphical lasso to estimate a shared precision matrix for interval-valued data and proves its sparsity and consistency.
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Rectification Difficulty and Optimal Sample Allocation in LLM-Augmented Surveys
A method using predicted rectification difficulty for optimal human sample allocation in LLM-augmented surveys captures 61-79% of theoretical efficiency gains and reduces MSE by 11% on two datasets without pilot data.
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Estimating Precision Matrices for High-Dimensional Interval-Valued Data
Introduces interval graphical lasso to estimate a shared precision matrix for interval-valued data and proves its sparsity and consistency.