Other modeling methods have also been proposed to mitigate the hallucination problem. Conjec-
turing that hallucinations can be caused by inattention to the source, Tian et al. [184] propose a
confidence score and a variational Bayes training framework to learn the score from data. Wang
et al. [199] introduce a table-text optimal-transport matching loss and an embedding similarity loss
to encourage faithfulness. The hallucination degree can also be treated as a controllable factor in
generating texts. In Filippova [50], the hallucination degree of each training sample is estimated

ACM Comput. Surv., Vol. 1, No. 1, Article . Publication date: February 2022.

Survey of Hallucination in Natural Language Generation

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and converted into a categorical value which is a part of the inputs as a controlled setting. This
approach does not require the dismissal of any input or modification of the model structure.
SurveyofHallucinationinNatural Language Generation