UKP-SQuARE v2 Explainability and Adversarial Attacks for Trustworthy QA. (arXiv:2208.09316v1 [cs.CL])

Question Answering (QA) systems are increasingly deployed in applications
where they support real-world decisions. However, state-of-the-art models rely
on deep neural networks, which are difficult to interpret by humans. Inherently
interpretable models or post hoc explainability methods can help users to
comprehend how a model arrives at its prediction and, if successful, increase
their trust in the system. Furthermore, researchers can leverage these insights
to develop new methods that are more accurate and less biased. In this paper,
we introduce …

arxiv attacks explainability qa trustworthy