3.2 Hallucination from Training and Inference
As discussed in the previous subsection, source-reference divergence existing in dataset is one of
the contributors of hallucination. However, Parikh et al. [140] show that hallucination problem
still occurs even when there is very little divergence in dataset. This is because there is another
contributor of hallucinations – training and modeling choices of neural models [85, 153, 159, 190].
Imperfect representation learning. The encoder has the role of comprehending and encoding
input text into meaningful representations. An encoder with a defective comprehension ability
could influence the degree of hallucination [140]. When encoders learn wrong correlations between
different parts of the training data, it could result in erroneous generation that diverges from the
input [3, 49, 103, 184].
SurveyofHallucinationinNatural Language Generation