[66] propose datasets that can benefit fact-checking systems specialized for dialogue systems. The
Conv-FEVER corpus [163] is a factual consistency detection dataset, which was created by adapting
the Wizard-of-Wikipedia dataset [32]. It consists of both factually consistent and inconsistent
responses and can be used to train a classifier to detect factually inconsistent responses with respect
to the knowledge provided.
8.2.4 Mitigation Methods. The hallucination issue can be mitigated by data pre-processing, which
includes introducing extra information into the data. Shen et al. [167] propose a measurement based
on seven attributes of the dialogue quality, including self-consistency. Based on this measurement,
the untrustworthy samples which get lower scores are filtered out from the training set to improve
the model performance in terms of self-consistency (i.e., intrinsic hallucination). Shuster et al. [168]
SurveyofHallucinationinNatural Language Generation