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.

翻译：问答模型面临复杂且开放式的上下文推理问题，但往往能通过利用训练数据中的数据集特定模式学习到高性能的解题启发式方法。这些模式（即"数据集伪影"）会削弱模型泛化至现实世界问答问题的能力。基于为问答任务训练的ElectraSmallDiscriminator模型，我们利用针对依赖伪影进行预测的模型设计的对抗挑战集，分析了数据集伪影的影响及其发生规律。在现有伪影影响缓解方法的基础上，我们提出"图谱接种"这一新型方法，通过对挑战数据的优化子集进行微调来降低模型对数据集伪影的依赖。研究表明，通过选择性地对挑战集中的模糊对抗样本进行微调，可在最小化模型对其他挑战性环境和问答数据集泛化能力损失的前提下，显著提升全挑战集的模型性能。