Labels or Preferences? Budget-Constrained Learning with Human Judgments over AI-Generated Outputs
This addresses the need for budget-conscious data acquisition strategies in AI, offering a principled solution for researchers and practitioners, though it is incremental in refining existing approaches.
The paper tackles the problem of optimally allocating a fixed annotation budget between ground-truth labels and pairwise preferences for AI-generated outputs, introducing a method that achieves asymptotic optimality and robustness in simulations and real-data analysis.
The increasing reliance on human preference feedback to judge AI-generated pseudo labels has created a pressing need for principled, budget-conscious data acquisition strategies. We address the crucial question of how to optimally allocate a fixed annotation budget between ground-truth labels and pairwise preferences in AI. Our solution, grounded in semi-parametric inference, casts the budget allocation problem as a monotone missing data framework. Building on this formulation, we introduce Preference-Calibrated Active Learning (PCAL), a novel method that learns the optimal data acquisition strategy and develops a statistically efficient estimator for functionals of the data distribution. Theoretically, we prove the asymptotic optimality of our PCAL estimator and establish a key robustness guarantee that ensures robust performance even with poorly estimated nuisance models. Our flexible framework applies to a general class of problems, by directly optimizing the estimator's variance instead of requiring a closed-form solution. This work provides a principled and statistically efficient approach for budget-constrained learning in modern AI. Simulations and real-data analysis demonstrate the practical benefits and superior performance of our proposed method.