Metrics such as semantic overlap [166], a learned evaluation metric based on BERT that models
human judgments, could be considered a better measurement of hallucination for GQA. Other
metrics such as the factual correctness can also be considered as a way to measure hallucination in
GQA. Zhang et al. [231] propose to explicitly measure the factual correctness of a generated text
against the reference by first extracting facts via an information extraction (IE) module. Then they
define and measure the factual accuracy score to be the ratio of facts in the generation text equal to
the corresponding facts in the reference.
SurveyofHallucinationinNatural Language Generation