Post-hoc Interpretability for Neural NLP: A Survey [article]

Andreas Madsen, Siva Reddy, Sarath Chandar
2022 arXiv   pre-print
Neural networks for NLP are becoming increasingly complex and widespread, and there is a growing concern if these models are responsible to use. Explaining models helps to address the safety and ethical concerns and is essential for accountability. Interpretability serves to provide these explanations in terms that are understandable to humans. Additionally, post-hoc methods provide explanations after a model is learned and are generally model-agnostic. This survey provides a categorization of
more » ... ow recent post-hoc interpretability methods communicate explanations to humans, it discusses each method in-depth, and how they are validated, as the latter is often a common concern.
arXiv:2108.04840v4 fatcat:twveq6lt7vgahi5fbibc4sue5e