Towards Robust Extractive Question Answering Models: Rethinking the Training Methodology
<|reference_start|>Towards Robust Extractive Question Answering Models: Rethinking the Training Methodology: This paper proposes a novel training method to improve the robustness of Extractive Question Answering (EQA) models. Previous research has shown that existing models, when trained on EQA datasets that include unanswerable questions, demonstrate a significant lack of robustness against distribution shifts and adversarial attacks. Despite this, the inclusion of unanswerable questions in EQA training datasets is essential for ensuring real-world reliability. Our proposed training method includes a novel loss function for the EQA problem and challenges an implicit assumption present in numerous EQA datasets. Models trained with our method maintain in-domain performance while achieving a notable improvement on out-of-domain datasets. This results in an overall F1 score improvement of 5.7 across all testing sets. Furthermore, our models exhibit significantly enhanced robustness against two types of adversarial attacks, with a performance decrease of only about a third compared to the default models.<|reference_end|>
@article{tran2024towards,
    title={Towards Robust Extractive Question Answering Models: Rethinking the
  Training Methodology},
    author={Son Quoc Tran, Matt Kretchmar},
    journal={arXiv preprint arXiv:2409.19766},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19766},
    primaryClass={cs.CL cs.AI}
}