Neural networks are capable of completing a wide range of tasks. Understanding how they arrive at their decisions, on the other hand, is frequently a mystery that goes unexplored. Explaining a neural model's decision process could have a significant social impact in domains where human oversight is crucial, like medical image processing and autonomous driving. These revelations could be instrumental in advising healthcare practitioners and possibly enhancing scientific breakthroughs. For a visual explanation of classifiers, there have been approaches such as attention maps.

In recent times, factoid open-domain question answering has witnessed significant progress, with the only requirement for answering a question being a short phrase. However, in the domain of long-form question answering, the level of efforts is comparatively less. LFQA holds significance primarily because it provides a testing ground for the measurement of the factuality of the text model. But the current metrics for evaluation are in need of more improvement in order to ensure LFQA progress.