Model-based Metric. Natural language has its natural on the flexibility of the surface forms
with the same semantics, so overlap-based metrics cannot provide the comprehensive evaluation.
Recently, several works have proposed evaluation metrics for measuring consistency, such as
using natural language inference (NLI) [40, 202], training learnable evaluation metrics [225], or
releasing an additional test set for coherence [10]. These methods are more flexible and supports
the generated responses with different surface forms. For the KGD task, Dziri et al. [42] propose the
BEGIN benchmark, which consists of samples taken from Dinan et al. [32] with additional human
annotation and a new classification task extending the NLI paradigm. Honovich et al. [73] present
a trainable metric for the KGD task, which also applies NLI. It is also noteworthy that Gupta et al.
[66] propose datasets that can benefit fact-checking systems specialized for dialogue systems. The
SurveyofHallucinationinNatural Language Generation