data, textual descriptions), the textual QG model generates synthetic problems based on the de-
scriptions. The structured data, textual descriptions (answers), and synthetic questions make up a
synthetic QG/QA dataset to train synthetic QA/QG models. Then, the synthetic QG model generates
questions based on the textual description to be evaluated. The synthetic QA model then generates
answers based on a synthetic question and the structured input data. Finally, BERTScore [229]
measures the similarity between the generated answer and description, indicating faithfulness.
SurveyofHallucinationinNatural Language Generation