Uncertainty Calibration for Ensemble-Based Debiasing Methods
This work addresses a specific bottleneck in debiasing for NLP tasks, offering an incremental improvement to existing ensemble methods.
The paper tackles the problem of inaccurate uncertainty estimations in bias-only models within ensemble-based debiasing methods, showing that calibration improves out-of-distribution accuracy on NLI and fact verification tasks.
Ensemble-based debiasing methods have been shown effective in mitigating the reliance of classifiers on specific dataset bias, by exploiting the output of a bias-only model to adjust the learning target. In this paper, we focus on the bias-only model in these ensemble-based methods, which plays an important role but has not gained much attention in the existing literature. Theoretically, we prove that the debiasing performance can be damaged by inaccurate uncertainty estimations of the bias-only model. Empirically, we show that existing bias-only models fall short in producing accurate uncertainty estimations. Motivated by these findings, we propose to conduct calibration on the bias-only model, thus achieving a three-stage ensemble-based debiasing framework, including bias modeling, model calibrating, and debiasing. Experimental results on NLI and fact verification tasks show that our proposed three-stage debiasing framework consistently outperforms the traditional two-stage one in out-of-distribution accuracy.