QA models are faced with complex and open-ended contextual reasoning
problems, but can often learn well-performing solution heuristics by exploiting
dataset-specific patterns in their training data. These patterns, or "dataset
artifacts", reduce the model's ability to generalize to real-world QA problems.
Utilizing an ElectraSmallDiscriminator model trained for QA, we analyze the
impacts and incidence of dataset artifacts using an adversarial challenge set
designed to confuse models reliant on artifacts for prediction. Extending
existing work on methods for mitigating artifact impacts, we propose
cartographic inoculation, a novel method that fine-tunes models on an optimized
subset of the challenge data to reduce model reliance on dataset artifacts. We
show that by selectively fine-tuning a model on ambiguous adversarial examples
from a challenge set, significant performance improvements can be made on the
full challenge dataset with minimal loss of model generalizability to other
challenging environments and QA datasets.

QA 模型在解决复杂的和开放性的情境推理问题时，可以通过利用训练数据中的特定模式学习高性能解决方案，但这些模式降低了模型推广到真实世界 QA 问题的能力。通过对 QA 模型进行对抗性挑战测试，我们分析了数据集特征对模型的影响和发生。在已有的减轻数据集特征影响方法的基础上，我们提出了一种名为 “地图疫苗接种” 的新方法，通过在优化的挑战数据子集上微调模型，减少模型对数据集特征的依赖。我们展示了通过有选择地在挑战测试中的模棱两可的对抗性例子上微调模型，可以在整个挑战数据集上取得显著的性能改进，并且对其他具有挑战性的环境和 QA 数据集的模型泛化能力损失最小。