New preprint on bias mitigation | Mingyu Derek Ma

In BMBI, we propose to mitigate bias exhibited in QA models by observing the query instance’s influence on another instance, enabling bias mitigation with extremely low resources. With our method, bias levels in multiple bias categories can be reduced without using category-specific instance-level annotation.

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

NAACL, 2024

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. 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.

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