Mitigating Bias for Question Answering Models by Tracking Bias Influence
Mingyu Derek Ma Jiun-Yu Kao Arpit Gupta Yu-Hsiang Lin Wenbo Zhao Tagyoung Chung Wei Wang Kai-Wei Chang Nanyun Peng
Models of various NLP tasks have been shown to exhibit stereotypes, and the
bias in the question answering (QA) models is especially harmful as the output
answers might be directly consumed by the end users. There have been datasets
to evaluate bias in QA models, while bias mitigation technique for the QA
models is still under-explored. In this work, we propose BMBI, an approach to
mitigate the bias of multiple-choice QA models. Based on the intuition that a
model would lean to be more biased if it learns from a biased example, we
measure the bias level of a query instance by observing its influence on
another instance. If the influenced instance is more biased, we derive that the
query instance is biased. We then use the bias level detected as an
optimization objective to form a multi-task learning setting in addition to the
original QA task. We further introduce a new bias evaluation metric to quantify
bias in a comprehensive and sensitive way. We show that our method could be
applied to multiple QA formulations across multiple bias categories. It can
significantly reduce the bias level in all 9 bias categories in the BBQ dataset
while maintaining comparable QA accuracy.