arise and propagated from either the QG model or the QA model.
4.2.3 Natural Language Inference (NLI) Metrics. There are not many labelled datasets for hallucina-
tion detection tasks, especially at the early stage when the hallucination problem starts to gain
attention. As an alternative, many works leverage the NLI dataset to tackle hallucinations. Note that
NLI is a task that determines whether a “hypothesis” is true (entailment), false (contradiction), or
undetermined (neutral) given a “premise”. These metrics are based on the idea that only the source
knowledge reference should entail the entirety of the information in faithful and hallucination-free
generation [39, 42, 45, 73, 76, 89, 93, 131, 206]. More specifically, NLI-based metrics define the hal-
lucination/faithfulness score to be the entailment probability between the source and its generated
text, also known as the percentage of times generated text entails, neutral to, and contradicts the
source.
SurveyofHallucinationinNatural Language Generation