Information Augmentation. It is intuitive that augmenting the inputs with external infor-
5.1.3
mation will obtain a better representation of the source. Because the external knowledge, explicit
alignment, extra training data, etc., can improve the correlation between the source and target and
help the model learn better task-related features. Consequently, a better semantic understanding
helps alleviate the divergence from the source issue. Examples of the augmented information
include entity information [114], extracted relation triples from source document [19, 74] obtained
by Fact Description Extraction, pre-executed operation results [136], synthetic data generated
through replacement or perturbation [21, 95], retrieved external knowledge [11, 46, 65, 168, 240],
and retrieved similar training samples [12].
SurveyofHallucinationinNatural Language Generation